CN116796503A

CN116796503A - Shale organic carbon content calculation model establishment method and device

Info

Publication number: CN116796503A
Application number: CN202310517457.9A
Authority: CN
Inventors: 任文希; 朱思融; 郭建春; 曾凡辉; 田助红; 周小金; 鄢雪梅; 谭浩; 童贞豪; 王天朋
Original assignee: Southwest Petroleum University
Current assignee: Southwest Petroleum University
Priority date: 2023-05-09
Filing date: 2023-05-09
Publication date: 2023-09-22

Abstract

The invention discloses a shale organic matter carbon content calculation model establishment method and device, and relates to the technical field of oil and gas exploitation. According to the method, shale without organic matters is simplified into a mixture of pores, clay and non-clay minerals, a relation principle of acoustic time difference and porosity is established based on a modified resistivity-porosity-saturation model, delta log R values of logging test data sets at different depths are calculated, multiple groups of data are divided into a training set and a testing set, a target shale organic matter carbon content calculation model is established through the training set, the testing set is combined with a first preset condition for verification, the shale organic matter carbon content calculation model of a target shale horizon is determined if the target shale organic matter carbon content calculation model is met, and TOC logging calculation is finally carried out on the target shale horizon according to the model. According to the method, the conductivity of the clay and the bound water in the clay are considered, the difference of acoustic wave time differences between clay minerals and other minerals is considered, and the established shale organic matter carbon content calculation model is more suitable for TOC calculation of shale gas reservoirs with high clay content.

Description

Shale organic carbon content calculation model establishment method and device

Technical Field

The invention relates to the technical field of oil and gas exploitation, in particular to a shale organic matter carbon content calculation model establishment method and device.

Background

The shale reservoir porosity and permeability are extremely low, the horizontal well multistage multi-cluster fracturing is one of key technologies for shale gas development, the geological dessert is combined to optimize the fracturing stage length and perforation positions, differential segmented design is developed, and a foundation can be laid for effective shale gas development. And shale organic carbon content (TOC) is one of the most important parameters for representing shale hydrocarbon production potential and gas content, so TOC acquisition has important significance for shale gas reservoir sweet spot optimization and perforation position optimization.

In the related art, a Δlog r model is often adopted to determine TOC, the model is to establish a theoretical relationship between acoustic time difference and resistivity of a water saturation sw=1 interval by using an Archie formula and a wyline time average equation, when a unit resistivity logarithmic value in a solid acoustic time difference range corresponds to the acoustic time difference of-50 μs/ft, that is, a superposition coefficient is 0.02, the Δlog r is calculated, and a TOC calculation model is established based on the Δlog r and a thermal index LOM, and the TOC of the target block is further calculated according to the calculation model.

However, since the clay and the conductivity of bound water therein are not considered in the derivation of the Δlog model, and the difference of the acoustic wave time difference between clay minerals and other minerals is not considered, the method is not suitable for TOC calculation of shale gas reservoirs with high clay content.

Disclosure of Invention

The embodiment of the invention provides a method and a device for establishing a shale organic matter carbon content calculation model, which are used for solving the problems that the clay and the conductivity of bound water therein are not considered in the derivation of a delta log R model, the difference of acoustic wave time differences between clay minerals and other minerals is not considered in the related technology, and the method and the device are not suitable for TOC calculation of shale gas reservoirs with high clay content. The technical proposal is as follows:

in a first aspect, a method for establishing a shale organic matter carbon content calculation model is provided, which comprises the following steps:

acquiring logging test data sets at N different depths of a target shale layer, wherein each logging test data set comprises a compensation neutron value, a density value, a natural gamma value, a sound wave time difference value, a stratum true resistivity logging response value, a clay content value, a clay resistivity value and an organic carbon content test value, and N is a positive integer greater than 10;

according to the compensated intermediate value, the density value and the clay content value in each logging test data set, a clay content logging calculation model is established, wherein the clay content logging calculation model is a model for reflecting the mapping relation between shale clay content indexes and the clay content values;

Determining a delta log r value for each of the well test data sets based on a modified resistivity-porosity-saturation model and a delta log r calculation model based on a sonic time difference and a theoretical relationship of resistivity based on a formation resistivity logging response value, a clay content value, and a clay resistivity value in each of the well test data sets;

selecting S logging test data sets, and establishing a target shale organic matter carbon content calculation model according to a compensation intermediate value, a density value, a clay content value, a delta log R value, a natural gamma value, an organic matter carbon content test value and the clay content logging calculation model of each logging test data set in the S logging test data sets, wherein S is a positive integer which is more than 1 and less than N;

and respectively testing and verifying the target shale organic matter carbon content calculation model by using each logging test data set in the unselected N-S logging test data sets, and determining the target shale organic matter carbon content calculation model as a shale organic matter carbon content calculation model of the target shale horizon if test and verification results all meet a first preset condition.

Optionally, the building a clay content logging calculation model according to the compensated intermediate value, the density value and the clay content value in each logging test data set comprises:

determining a clay content index of each well logging test data set according to the compensated intermediate value and the density value in each well logging test data set;

and establishing a clay content logging calculation model through a least square method according to the clay content index and the clay content value of each logging test data set.

Optionally, the determining the clay content index of each of the well-logging test data sets according to the compensated intermediate value and the density value in each of the well-logging test data sets includes:

determining a clay content index for each of said well logging test data sets from said compensated intermediate values and density values in each of said well logging test data sets by the following formula,

wherein I is _cl Is clay content index, v/v; CNL is the compensation meson value,%; DEN is the density value, g/cm ³ ；DEN _limestone For limestone density, g/cm ³ Take the value of 2.71g/cm ³ ；DEN _f For pore fluid density, g/cm ³ The value is 1g/cm ³ 。

Optionally, the building a clay content logging calculation model according to the clay content index and the clay content value of each logging test data set through a least square method comprises the following steps:

Fitting by a least square method based on the following formula according to the clay content index and the clay content value of each logging test data set, determining the mapping relation between the fitted clay content index and the clay content value as a clay content logging calculation model,

wherein V is _sh Is the clay content value, v/v; (V) _sh ) _log Calculating the value, v/v, of clay content; i _cl Is clay content index, v/v; a, a ₁ 、a ₂ Is a fitting parameter.

Optionally, determining the Δlogr value for each of the well logging test data sets based on the corrected resistivity-porosity-saturation model and the Δlogr calculation model from the acoustic time difference value, the formation resistivity logging response value, the clay content value, and the clay resistivity value in each of the well logging test data sets comprises:

determining a corresponding acoustic time difference value and a formation true resistivity logging response value in each of the log test data sets when the constraint value is minimum based on a modified resistivity-porosity-saturation model and the constraint as described below based on the acoustic time difference value, the formation true resistivity logging response value, the clay content value, and the clay resistivity value in each of the log test data sets,

Wherein R is _o Logging response value, ohm, for true resistivity of the stratum which does not contain organic matters and has water saturation of 1; v (V) _sh Is the clay content value, v/v; r is R _sh Clay resistivity values, ohm; r is R _w The value of the resistivity of the formation water is 0.1 ohm; Δt is the difference in acoustic wave time, μs/ft; Δt (delta t) _f The difference value is mu s/ft, and the value is 189 mu s/ft; Δt (delta t) _sh Is the difference of clay sound wave timeMu s/ft, value 89.407 mu s/ft; Δt (delta t) _m The difference value of acoustic wave time of the matrix formed by other minerals of the shale except clay minerals is mu s/ft, and the value is 55 mu s/ft; a is lithology coefficient, and the value is 1; Δt (delta t) _i A μ s/ft for any acoustic time difference in the modified resistivity-porosity-saturation model; rt (Rt) _i Logging response values, ohm, for true formation resistivity in the modified resistivity-porosity-saturation model as a function of any sonic time difference;

determining the acoustic wave time difference value and the true formation resistivity logging response value corresponding to the minimum constraint condition value as a target acoustic wave time difference value and a target formation true resistivity logging response value;

determining a delta log R value for each of the well logging test data sets based on a delta log R calculation model based on the acoustic time difference value, the formation true resistivity well logging response value, the clay content value, the clay resistivity value, the target acoustic time difference value, and the target formation true resistivity well logging response value in each of the well logging test data sets,

Wherein R is _t Logging response values for true resistivity of the formation, ohm; r is R _tb1 Logging response values for true resistivity of the target formation, ohm; Δt is the difference in acoustic wave time, μs/ft; Δt (delta t) _b1 Is the difference value of the target sound wave time, mu s/ft; m is a cementation index, and the value is 2; v (V) _sh Is the clay content value, v/v; r is R _sh Clay resistivity values, ohm; r is R _w The value of the resistivity of the formation water is 0.1 ohm; Δt (delta t) _f The difference value is mu s/ft, and the value is 189 mu s/ft; Δt (delta t) _sh The clay sound wave time difference value is mu s/ft, and the value is 89.407 mu s/ft; Δt (delta t) _m The difference value of acoustic wave time of the matrix formed by other minerals of the shale except clay minerals is mu s/ft, and the value is 55 mu s/ft; a is a lithology coefficient, and the value is 1.

Optionally, the building a target shale organic carbon content calculation model according to the compensated intermediate value, the density value, the clay content value, the Δlog r value, the natural gamma value, the organic carbon content test value, and the clay content logging calculation model of each of the S logging test data sets comprises:

according to the compensated intermediate value, the density value, the clay content value, the delta log R value, the natural gamma value, the organic matter carbon content test value and the clay content logging calculation model of each logging test data set in S logging test data sets, performing linear regression analysis fitting based on the following calculation model, determining the fitted model as a target shale organic matter carbon content calculation model,

Wherein TOC is the organic carbon content test value,%; (TOC) _log Calculated value of carbon content of organic matters,%; Δlogr is a Δlogr value; GR is a natural gamma value, API; (V) _sh ) _log Calculating the value, v/v, of clay content; b ₁ 、b ₂ 、b ₃ 、b ₄ 、b ₅ Is a fitting parameter.

Optionally, the first preset condition means that each of the N-S logging test data sets that are not selected respectively performs test verification on the target shale organic matter carbon content calculation model, and a difference between an organic matter carbon content calculation value and the organic matter carbon content test value obtained by test verification results is smaller than 0.5.

Optionally, after determining the target shale organic matter carbon content calculation model as the shale organic matter carbon content calculation model of the target shale horizon, the method further includes:

acquiring a logging test prediction data set of a target shale horizon, wherein the logging test prediction data set comprises a compensation neutron value, a density value, a natural gamma value, a sonic time difference value, a stratum true resistivity logging response value, a clay content value and a clay resistivity value;

and determining the organic matter carbon content of the target shale horizon according to the shale organic matter carbon content calculation model of the target shale horizon and the logging test prediction data set.

In a second aspect, a shale organic matter carbon content calculation model building device is provided, including:

the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring logging test data sets at N different depths of a target shale horizon, each logging test data set comprises a compensation neutron value, a density value, a natural gamma value, a sound wave time difference value, a stratum true resistivity logging response value, a clay content value, a clay resistivity value and an organic carbon content test value, and N is a positive integer greater than 10;

the first modeling module is used for establishing a clay content well logging calculation model according to the compensation intermediate value, the density value and the clay content value in each well logging test data set, wherein the clay content well logging calculation model is a model for reflecting the mapping relation between shale clay content indexes and the clay content values;

a first determining module, configured to determine a Δlogr value for each of the well logging test data sets based on a modified resistivity-porosity-saturation model and a Δlogr calculation model based on a sonic time difference value, a formation true resistivity logging response value, a clay content value, and a clay resistivity value in each of the well logging test data sets, where the modified resistivity-porosity-saturation model and the Δlogr calculation model are models based on a sonic time difference and a resistivity theoretical relationship;

The second modeling module is used for selecting S logging test data sets, and establishing a target shale organic matter carbon content calculation model according to the compensated intermediate value, the density value, the clay content value, the delta log R value, the natural gamma value, the organic matter carbon content test value and the clay content logging calculation model of each logging test data set in the S logging test data sets, wherein S is a positive integer greater than 1 and less than N;

the verification module is used for respectively carrying out test verification on the target shale organic matter carbon content calculation model by utilizing each of the N-S logging test data sets which are not selected, and if the test verification results meet a first preset condition, determining the target shale organic matter carbon content calculation model as a shale organic matter carbon content calculation model of the target shale horizon.

Optionally, the first modeling module includes:

a first determining unit, configured to determine a clay content index of each of the logging test data sets according to the compensated intermediate value and the density value in each of the logging test data sets;

and the first modeling unit is used for establishing a clay content well logging calculation model through a least square method according to the clay content index and the clay content value of each well logging test data set.

Optionally, the first determining unit specifically includes:

Optionally, the first modeling unit specifically includes:

Optionally, the first determining module includes:

a second determination unit configured to determine, based on the acoustic time difference value, the formation true resistivity logging response value, the clay content value, and the clay resistivity value in each of the logging test data sets, a corresponding acoustic time difference value and formation true resistivity logging response value in the modified resistivity-porosity-saturation model when the constraint condition value is minimum in each of the logging test data sets based on a modified resistivity-porosity-saturation model and constraint conditions as described below,

Wherein R is _o Logging response value, ohm, for true resistivity of the stratum which does not contain organic matters and has water saturation of 1; v (V) _sh Is the clay content value, v/v; r is R _sh Clay resistivity values, ohm; r is R _w The value of the resistivity of the formation water is 0.1 ohm; Δt is the difference in acoustic wave time, μs/ft; Δt (delta t) _f The difference value is mu s/ft, and the value is 189 mu s/ft; Δt (delta t) _sh The clay sound wave time difference value is mu s/ft, and the value is 89.407 mu s/ft; Δt (delta t) _m The difference value of acoustic wave time of the matrix formed by other minerals of the shale except clay minerals is mu s/ft, and the value is 55 mu s/ft; a is lithology coefficient, and the value is 1; Δt (delta t) _i A μ s/ft for any acoustic time difference in the modified resistivity-porosity-saturation model; rt (Rt) _i For the modified resistivity-porosity-saturation model as a function of any acoustic time differenceIs a true resistivity log response value, ohm;

the third determining unit is used for determining the acoustic wave time difference value and the true formation resistivity logging response value corresponding to the minimum constraint condition value as the target acoustic wave time difference value and the true formation resistivity logging response value;

a fourth determination unit configured to determine a Δlog r value for each of the well logging test data sets based on a Δlog r calculation model based on the acoustic wave time difference value, the formation true resistivity logging response value, the clay content value, the clay resistivity value, the target acoustic wave time difference value, and the target formation true resistivity logging response value in each of the well logging test data sets,

Optionally, the second modeling module specifically includes:

Optionally, the first preset condition in the verification module means that each of the N-S logging test data sets that are not selected performs test verification on the target shale organic matter carbon content calculation model, and a difference between an organic matter carbon content calculation value and the organic matter carbon content test value obtained by test verification results is smaller than 0.5.

Optionally, the shale organic matter carbon content calculation model building device further comprises:

the second acquisition module is used for acquiring a logging test prediction data set of the target shale horizon, wherein the logging test prediction data set comprises a compensation neutron value, a density value, a natural gamma value, a sound wave time difference value, a stratum true resistivity logging response value, a clay content value and a clay resistivity value;

and the second determining module is used for determining the organic matter carbon content of the target shale layer according to the shale organic matter carbon content calculation model of the target shale layer and the logging test prediction data set.

In a third aspect, a shale organic matter carbon content calculation model building device is provided, the device includes:

a processor and a memory for storing processor-executable instructions;

wherein the processor is configured to perform any of the methods of the first aspect above.

The technical scheme provided by the embodiment of the application at least has the following beneficial effects:

in the embodiment of the application, logging test data sets at different depths of a target shale horizon are acquired, clay content logging calculation models for reflecting the mapping relation between a shale clay content index and a clay content value are established according to compensation intermediate sub-values, density values and clay content values in each logging test data set, target organic matter carbon content calculation models are established according to acoustic wave time difference values, formation true resistivity logging response values, clay content values and clay resistivity values in each logging test data set, delta log R values of each logging test data set are determined based on the corrected resistivity-porosity-saturation model and delta log R calculation models, S logging test data sets are selected, and shale content calculation conditions are verified if the target organic matter carbon content calculation models meet the target shale content calculation conditions by using N-S logging test data sets. The basic idea of the method is that shale without organic matters is simplified into a mixture of pores, clay and non-clay minerals, a relation principle of acoustic wave time difference and porosity is established based on a modified resistivity-porosity-saturation model, delta log R values of logging test data sets at different depths are calculated, a plurality of groups of logging test data sets are divided into a training set and a testing set, a target shale organic matter carbon content calculation model is established through the training set, the testing set is combined with a first preset condition for verification, if the shale organic matter carbon content calculation model of a target shale horizon is met, TOC logging calculation is finally carried out on the target shale horizon according to the shale organic matter carbon content calculation model. According to the application, the traditional theoretical relationship between acoustic wave time difference and resistivity is improved by considering the conductivity of clay and bound water therein and the difference between acoustic wave time difference of clay minerals and other minerals, so that the established shale organic matter carbon content calculation model is more suitable for TOC calculation of shale gas reservoirs with high clay content.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic flow chart of a shale organic matter carbon content calculation model establishment method provided by an embodiment of the application;

FIG. 2 is a schematic flow chart of another method for establishing a shale organic matter carbon content calculation model provided by the embodiment of the application;

FIG. 3 is a schematic structural diagram of a shale organic matter carbon content calculation model building device provided by the embodiment of the application;

fig. 4 is a schematic structural diagram of a terminal 400 according to an embodiment of the present application.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the embodiments of the present application will be described in further detail with reference to the accompanying drawings.

Before explaining the embodiments of the present application in detail, the nouns, application scenarios and system architectures involved in the embodiments of the present application are respectively explained.

First, nouns involved in the embodiments of the present invention will be described.

Compensating for a mesovalue

The compensated neutron value is a log value obtained using a compensated neutron logging method. An isotope source neutron logging method for compensating neutron logging utilizes dual detectors to eliminate the effect of a borehole on measurement results. And (3) an isotope neutron source (18 Ci americium-beryllium neutron source) for compensating neutron logging emits fast neutrons to a stratum in a shaft, and then two thermal neutron detectors with different long and short source distances are used for measuring thermal neutrons which are moderated by the stratum and scattered back to the shaft, so that two counting rates are obtained. The ratio of the short source distance (26 cm) to the long source distance (38 cm) detector count rate mainly reflects the deceleration capability of the stratum to fast neutrons and shows the change of the stratum hydrogen content

Density value

The density value is the mass per unit volume of shale at the shale level.

Natural gamma value

The natural gamma value is a log value obtained using a natural gamma logging method. What natural gamma logs are count rates or normalized readings from all gamma photons with energies greater than 100 keV.

Time difference of sound wave

The sonic time difference value is a log value obtained by a sonic time difference logging method. Acoustic time difference logging is to put a controlled acoustic vibration source into the well, and the acoustic wave emitted by the acoustic source causes the surrounding particles to vibrate, so that bulk waves, namely longitudinal waves and transverse waves, are generated in the stratum, and induced interface waves, namely pseudo Rayleigh waves and Stoneley waves, are generated on the interface between the well wall and drilling fluid. These waves act as carriers of formation information and are received by downhole receivers and sent to the surface for recording, i.e., sonic logging. Sonic logging is classified into sonic logging and sonic logging. The acoustic moveout value of the present invention comes from sonic logging.

True resistivity logging response value of stratum

The true resistivity logging response value of the stratum is obtained by correcting the double lateral logging data through three influencing factors of the well bore, the surrounding rock-layer thickness and the invasion.

Clay content value

The clay content value is the content of clay minerals in the stratum, the clay minerals comprise kaolinite, montmorillonite, chlorite, illite and the like, and the clay content value can be obtained through analysis and test of X-ray diffraction all-rock minerals.

Clay resistivity value

Clay resistivity refers to the degree of resistance of clay to current flow, and clay resistivity is related to its composition, water content, temperature, etc. The different kinds of clay differ in composition and therefore in resistivity.

Carbon content test value of organic matter

The organic carbon content test value refers to the mass of organic carbon in a unit mass of rock, and is generally expressed as mass fraction (%), and can be measured by combustion oxidation-non-dispersive infrared absorption method

Secondly, an application scenario related to the embodiment of the invention is described.

Before fracturing and perforating shale blocks with high clay content, the shale organic matter carbon content (TOC) of different layers needs to be determined, then the fracturing section length and the perforating position are optimized according to TOC data, and differential segmentation design is carried out.

Finally, a system architecture according to an embodiment of the present invention is described.

The shale organic matter carbon content calculation model establishment method provided by the embodiment of the invention can be applied to a terminal, and the terminal has a data processing function. Specifically, the terminal may be a smart phone, a tablet computer, a notebook computer, a desktop computer or other terminals capable of performing data processing.

Fig. 1 is a schematic flow chart of a shale organic matter carbon content calculation model establishment method provided by an embodiment of the invention. Referring to fig. 1, the method comprises the steps of:

step 101: and acquiring logging test data sets at N different depths of the target shale layer, wherein each logging test data set comprises a compensation neutron value, a density value, a natural gamma value, a sound wave time difference value, a stratum true resistivity logging response value, a clay content value, a clay resistivity value and an organic carbon content test value, and N is a positive integer greater than 10.

Step 102: and building a clay content well logging calculation model according to the compensated intermediate value, the density value and the clay content value in each well logging test data set, wherein the clay content well logging calculation model is a model for reflecting the mapping relation between the shale clay content index and the clay content value.

Step 103: determining a delta log r value for each well logging test data set based on the modified resistivity-porosity-saturation model and the delta log r calculation model based on the acoustic time difference value, the formation true resistivity logging response value, the clay content value, and the clay resistivity value in each well logging test data set, the modified resistivity-porosity-saturation model and the delta log r calculation model being models based on the acoustic time difference and the resistivity theoretical relationship.

Step 104: s logging test data sets are selected, and a target shale organic carbon content calculation model is established according to the compensation intermediate value, the density value, the clay content value, the delta log R value, the natural gamma value, the organic carbon content test value and the clay content logging calculation model of each logging test data set in the S logging test data sets, wherein S is a positive integer which is more than 1 and less than N.

Step 105: and respectively testing and verifying the target shale organic matter carbon content calculation model by using each logging test data set in the unselected N-S logging test data sets, and determining the target shale organic matter carbon content calculation model as a shale organic matter carbon content calculation model of the target shale horizon if the testing and verification results all meet a first preset condition.

In the embodiment of the invention, shale without organic matters is simplified into a mixture of pores, clay and non-clay minerals, a relation principle of acoustic wave time difference and porosity is established based on a modified resistivity-porosity-saturation model, delta log R values of logging test data sets at different depths are calculated, a plurality of groups of logging test data sets are divided into a training set and a testing set, a target shale organic matter carbon content calculation model is established through the training set, the testing set is combined with a first preset condition for verification, if the shale organic matter carbon content calculation model of a target shale horizon is determined, and TOC logging calculation is finally carried out on the target shale horizon according to the model. According to the invention, the traditional theoretical relationship between acoustic wave time difference and resistivity is improved by considering the conductivity of clay and bound water therein and the difference between acoustic wave time difference of clay minerals and other minerals, so that the established shale organic matter carbon content calculation model is more suitable for TOC calculation of shale gas reservoirs with high clay content.

Optionally, building a clay content log calculation model from the compensated neutron value, the density value, and the clay content value in each log test data set, including:

Determining a clay content index for each well logging test data set according to the compensated intermediate value and the density value in each well logging test data set;

and establishing a clay content well logging calculation model through a least square method according to the clay content index and the clay content value of each well logging test data set.

Optionally, determining the clay content index for each of the well test data sets based on the compensated intermediate value and the density value in each of the well test data sets comprises:

determining a clay content index for each well logging test data set based on the compensated intermediate value and the density value in each well logging test data set by the following formula,

Optionally, building a clay content logging calculation model according to the clay content index and the clay content value of each logging test data set through a least square method, including:

Optionally, determining a Δlog r value for each well logging test data set based on the modified resistivity-porosity-saturation model and the Δlog r calculation model from the acoustic time difference value, the formation resistivity log response value, the clay content value, and the clay resistivity value in each well logging test data set, comprising:

determining a corresponding acoustic wave time difference value and a formation true resistivity logging response value in each well logging test data set when the constraint condition value is minimum based on the resistivity-porosity-saturation model and the constraint condition corrected as follows according to the acoustic wave time difference value, the formation true resistivity logging response value, the clay content value and the clay resistivity value in each well logging test data set,

wherein R is _o Is free of organic matterTrue resistivity log response value, ohm, for a formation with a water saturation of 1; v (V) _sh Is the clay content value, v/v; r is R _sh Clay resistivity values, ohm; r is R _w The value of the resistivity of the formation water is 0.1 ohm; Δt is the difference in acoustic wave time, μs/ft; Δt (delta t) _f The difference value is mu s/ft, and the value is 189 mu s/ft; Δt (delta t) _sh The clay sound wave time difference value is mu s/ft, and the value is 89.407 mu s/ft; Δt (delta t) _m The difference value of acoustic wave time of the matrix formed by other minerals of the shale except clay minerals is mu s/ft, and the value is 55 mu s/ft; a is lithology coefficient, and the value is 1; Δt (delta t) _i For any acoustic time difference in the modified resistivity-porosity-saturation model, μs/ft; rt (Rt) _i Logging response values, ohm, for true formation resistivity in the modified resistivity-porosity-saturation model as a function of any sonic time difference;

determining the acoustic wave time difference value and the formation true resistivity logging response value corresponding to the minimum constraint condition value as the target acoustic wave time difference value and the target formation true resistivity logging response value;

determining a delta log R value for each well logging test data set based on a delta log R calculation model based on the acoustic time difference value, the formation true resistivity logging response value, the clay content value, the clay resistivity value, the target acoustic time difference value, and the target formation true resistivity logging response value in each well logging test data set,

wherein R is _t Logging response values for true resistivity of the formation, ohm; r is R _tb1 Logging response values for true resistivity of the target formation, ohm; Δt is the difference in acoustic wave time, μs/ft; Δt (delta t) _b1 Is the difference value of the target sound wave time, mu s/ft; m is a cementation index, and the value is 2; v (V) _sh Is the clay content value, v/v; r is R _sh Clay resistivity values, ohm; r is R _w For the formation water resistivity to be high,an ohm m value of 0.1ohm m; Δt (delta t) _f The difference value is mu s/ft, and the value is 189 mu s/ft; Δt (delta t) _sh The clay sound wave time difference value is mu s/ft, and the value is 89.407 mu s/ft; Δt (delta t) _m The difference value of acoustic wave time of the matrix formed by other minerals of the shale except clay minerals is mu s/ft, and the value is 55 mu s/ft; a is a lithology coefficient, and the value is 1.

Optionally, building a target shale organic carbon content calculation model from the compensated neutron value, the density value, the clay content value, the Δlog r value, the natural gamma value, the organic carbon content test value, and the clay content logging calculation model for each of the S well logging test data sets, comprising:

according to the compensated intermediate value, the density value, the clay content value, the delta log R value, the natural gamma value, the organic matter carbon content test value and the clay content well logging calculation model of each of the S well logging test data sets, carrying out linear regression analysis fitting based on the following calculation models, determining the fitted model as a target shale organic matter carbon content calculation model,

Optionally, the first preset condition means that each of the N-S logging test data sets that are not selected respectively performs test verification on the target shale organic matter carbon content calculation model, and a difference between the organic matter carbon content calculation value and the organic matter carbon content test value obtained by the test verification result is smaller than 0.5.

Optionally, after determining the target shale organic matter carbon content calculation model as the shale organic matter carbon content calculation model of the target shale horizon, further comprises:

and determining the organic matter carbon content of the target shale horizon according to the shale organic matter carbon content calculation model and the logging test prediction data set of the target shale horizon.

All the above optional technical solutions may be combined according to any choice to form an optional embodiment of the present invention, and the embodiments of the present invention will not be described in detail.

Fig. 2 is a schematic flow chart of another method for establishing a shale organic matter carbon content calculation model according to an embodiment of the invention. Referring to fig. 2, the method includes the steps of:

step 201: and acquiring logging test data sets at N different depths of the target shale layer, wherein each logging test data set comprises a compensation neutron value, a density value, a natural gamma value, a sound wave time difference value, a stratum true resistivity logging response value, a clay content value, a clay resistivity value and an organic carbon content test value, and N is a positive integer greater than 10.

The compensation neutron value, the density value, the natural gamma value, the acoustic time difference value and the true formation resistivity logging response value are all test values obtained after logging the target shale horizon. For example, the compensated neutron value is a compensated neutron value of the horizon obtained after logging the target shale horizon by using a compensated neutron logging method, the density value and the natural gamma value are the density value and the natural gamma value of the horizon obtained after logging the target shale horizon by using a natural gamma logging method, the acoustic time difference value is an acoustic time difference value of the horizon obtained after logging the target shale horizon by using an acoustic time difference logging method, and the formation true resistivity logging response value and the clay resistivity value are the formation true resistivity logging response value of the horizon obtained after logging the target shale horizon by using a resistivity logging method. In practical application, the above values may be obtained by directly sending the test result to the device by the logging instrument, or by user input, which is not particularly limited in the embodiment of the present invention.

Wherein, the clay content value and the organic carbon content test value are values obtained by laboratory tests. For example, the clay content value can be determined according to an all-rock X-ray diffraction experiment, and the organic carbon content test value TOC can be determined according to a combustion oxidation-non-dispersive infrared absorption method. In practical application, the above values may be obtained by sending the results to the device by the experimental apparatus, or may be obtained by user input, which is not particularly limited in the embodiment of the present invention.

It should be further noted that, the logging test data sets each include depth information, where the depth information may be an actual depth of the shale layer, or may be continuous depth information calibrated by serial numbers, where the depth information in the logging test data sets at N different depths is different. For example, when sequential depth information is numbered, the difference in depth information in the logging test data sets at 10 different depths may be represented as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. In addition, in order to improve the model accuracy of the shale organic matter carbon content calculation, the number of the logging test data sets is not less than 10, and the model accuracy is higher as the number is larger, and the upper limit of the number of the logging test data sets is not limited in the embodiment of the invention.

Step 202: and building a clay content well logging calculation model according to the compensated intermediate value, the density value and the clay content value in each well logging test data set, wherein the clay content well logging calculation model is a model for reflecting the mapping relation between the shale clay content index and the clay content value.

It should be noted that, in the embodiment of the present invention, in order to build a TOC calculation model applicable to a shale reservoir with a higher clay content, when a TOC technical model is built, a mapping relationship between an actual clay content measured value and a calculated clay content value of a shale layer at each depth is first built. When the mapping relation is established, model fitting can be directly carried out according to a clay content well logging calculation formula, the clay content index of each well logging test data set can be calculated first, then the clay content index and the clay content value of each well logging test data set are fitted on the basis of the clay content well logging calculation formula, and a clay content well logging calculation model is established.

When the clay content index of each logging test data set is calculated firstly, then the clay content index and the clay content value of each logging test data set are fitted based on a calculation formula according to the clay content logging, and a clay content logging calculation model is established, the concrete steps are as follows:

Step 2021: and determining the clay content index of each well logging test data set according to the compensated intermediate value and the density value in each well logging test data set.

It should be noted that, the neutron logging instrument is usually calibrated by using the porosity of limestone as a standard scale, and the porosity in the embodiment of the invention is that of the calcite as the rock matrix. The apparent neutron porosity of clay minerals is typically greater than the apparent density porosity. Therefore, the clay content index can be calculated by using the density value and the compensation intermediate value in each logging test data set, and the specific formula is as follows:

Step 2022: and establishing a clay content well logging calculation model through a least square method according to the clay content index and the clay content value of each well logging test data set.

That is, after determining the clay content index in each logging test data set, substituting the clay content value in each logging test data set and the calculated clay content index into the above formula, and fitting by using a least square method to obtain a formula optimal a ₁ 、a ₂ Fitting the parameter values, and then adding a to the obtained product ₁ 、a ₂ And substituting the fitting parameter values into the formula to obtain the clay content well logging calculation model. In the actual fitting process, an lsqcurvefit function in MATLAB may be called for fitting.

Step 203: determining a delta log r value for each well logging test data set based on the modified resistivity-porosity-saturation model and the delta log r calculation model based on the acoustic time difference value, the formation true resistivity logging response value, the clay content value, and the clay resistivity value in each well logging test data set, the modified resistivity-porosity-saturation model and the delta log r calculation model being models based on the acoustic time difference and the resistivity theoretical relationship.

Specifically, the Δlog R value for each log test data set may be determined by steps 2031-2033 as follows.

Step 2031: determining a corresponding acoustic wave time difference value and a formation true resistivity logging response value in each well logging test data set when the constraint condition value is minimum based on the resistivity-porosity-saturation model and the constraint condition corrected as follows according to the acoustic wave time difference value, the formation true resistivity logging response value, the clay content value and the clay resistivity value in each well logging test data set,

wherein R is _o Logging response value, ohm, for true resistivity of the stratum which does not contain organic matters and has water saturation of 1; v (V) _sh Is the clay content value, v/v; r is R _sh Clay resistivity values, ohm; r is R _w The value of the resistivity of the formation water is 0.1 ohm; Δt is the difference in acoustic wave time, μs/ft; Δt (delta t) _f The difference value is mu s/ft, and the value is 189 mu s/ft; Δt (delta t) _sh The clay sound wave time difference value is mu s/ft, and the value is 89.407 mu s/ft; Δt (delta t) _m The difference value of acoustic wave time of the matrix formed by other minerals of the shale except clay minerals is mu s/ft, and the value is 55 mu s/ft; a is lithology coefficient, and the value is 1; Δt (delta t) _i For any acoustic time difference in the modified resistivity-porosity-saturation model, μs/ft; rt (Rt) _i The resistivity log response, ohm, is the true resistivity of the formation in the modified resistivity-porosity-saturation model as a function of any acoustic time difference.

The principle of the above steps is as follows:

the corrected resistivity-porosity-saturation model is used to define the relationship between true resistivity and porosity of the formation.

Wherein R is _t Logging response values for true resistivity of the formation, ohm; v (V) _sh Is the clay content value, v/v; s is S _w Is water saturation, v/v; r is R _sh Clay resistivity, ohm; phi is the porosity, v/v; m is a cementation index, and the value is 2; n is a saturation index; a is lithology coefficient, and the value is 1; r is R _w The value of 0.1ohm is given for the formation water resistivity, ohm.

For water saturation S _w Layer segment=1, simplified, deformed, obtained,

where Ro is the shale formation resistivity, ohm, without organic matter and with a water saturation of 1.

Taking logarithm from two opposite sides to obtain the formula

log ₁₀ R _o ＝-log ₁₀ [aV _sh R _w (1-V _sh )+φ ^m R _sh ]+log ₁₀ [aR _sh R _w (1-V _sh )]

The shale reservoir has high clay content, complex mineral composition and strong heterogeneity, and in addition, the difference of acoustic time differences of different minerals is large, as shown in table 1.

TABLE 1 shale reservoir common mineral theory acoustic time difference response values

The difference of the sound wave time difference value of the clay and the quartz is larger, which is about 1.6 times of the sound wave time difference of the quartz; the differences in sonic times of calcite, dolomite and pyrite with quartz are small. Therefore, the shale volume model without organic matters can be simplified into three parts of pores, clay and non-clay.

The relationship between the acoustic time difference and the porosity of the shale without organic matter can be expressed as:

Δt＝φΔt _f +V _sh Δt _sh +(1-φ-V _sh )Δt _m

wherein, deltat is the acoustic time difference, mu s/ft; Δt (delta t) _f Taking the value of 189 mu s/ft for the pore fluid acoustic time difference mu s/ft; Δt (delta t) _sh The clay sound wave time difference is mu s/ft, and the value is 89.407 mu s/ft; Δt (delta t) _m The acoustic time difference of the matrix is 55 mu s/ft for the shale other than clay mineral.

Thus, shale porosity without organic matter can be expressed as:

carry-in log of the above type ₁₀ R _o ＝-log ₁₀ [aV _sh R _w (1-V _sh )+φ ^m R _sh ]+log ₁₀ [aR _sh R _w (1-V _sh )]And obtaining a shale interval acoustic time difference-resistivity theoretical relation which does not contain organic matters and has water saturation of 1, wherein the theoretical relation is a corrected resistivity-porosity-saturation model:

it should be further noted that, in determining, according to the modified resistivity-porosity-saturation model and the constraint condition, a specific principle when determining, in each logging test data set, a corresponding acoustic time difference value and a true formation resistivity logging response value in the modified resistivity-porosity-saturation model when the constraint condition value is minimum is as follows:

and constructing a coordinate system by taking the acoustic wave time difference as a horizontal axis and the resistivity logarithmic value as a vertical axis. For each well logging test data set, using the acoustic time difference value and the true formation resistivity logging response value in each well logging test data set, respectively, making points A (deltat, log) in the constructed coordinate system ₁₀ R _t )；

And further drawing an acoustic time difference-resistivity theoretical relation curve of the logging test data set in the coordinate system according to the corrected resistivity-porosity-saturation model and the calculated clay content calculated by the clay content logging calculation model constructed in the step 2022.

By means of constraint conditionsDetermining the shortest distance point (deltat) on the theoretical relationship between acoustic time difference and resistivity of the A point and the logging test data set _bl ，log ₁₀ R _tbl ) The constraint values are minimized.

Step 2032: and determining the acoustic wave time difference value and the formation true resistivity logging response value corresponding to the minimum constraint condition value as the target acoustic wave time difference value and the target formation true resistivity logging response value.

Step 2033: determining a delta log R value for each well logging test data set based on a delta log R calculation model based on the acoustic time difference value, the formation true resistivity logging response value, the clay content value, the clay resistivity value, the target acoustic time difference value, and the target formation true resistivity logging response value in each well logging test data set,

It should be noted that step 203 is performed once for each well test data set to determine the Δlog r value of each well test data set.

Step 204: s logging test data sets are selected, and a target shale organic carbon content calculation model is established according to the compensation intermediate value, the density value, the clay content value, the delta log R value, the natural gamma value, the organic carbon content test value and the clay content logging calculation model of each logging test data set in the S logging test data sets, wherein S is a positive integer which is more than 1 and less than N.

It should be noted that, in determining the clay content well logging calculation model and the delta of each well logging test data setAnd the log R value can establish a direct mapping relation among the calculated value of the organic carbon content, the calculated value of the clay content, the natural gamma value and the delta log R value. In order to fit the relation among the parameters, a shale organic matter carbon content calculation model is established, and S logging test data sets are selected from N logging test data sets to serve as training sets for parameter fitting. Specifically, in parameter fitting, linear regression analysis fitting can be performed based on the following calculation model, and the determined b after fitting can be obtained ₁ 、b ₂ 、b ₃ 、b ₄ 、b ₅ And substituting the five fitting parameters into the model to determine the model as a target shale organic carbon content calculation model. In the actual fitting process, the linear regression analysis method may be Linear Regression linear regression, logistic Regression logistic regression or the like, which is not particularly limited in the embodiment of the present invention.

Step 205: and respectively testing and verifying the target shale organic matter carbon content calculation model by using each logging test data set in the unselected N-S logging test data sets, and determining the target shale organic matter carbon content calculation model as a shale organic matter carbon content calculation model of the target shale horizon if the testing and verification results all meet a first preset condition.

After the target shale organic matter carbon content calculation model is established, test verification analysis is carried out on the target shale organic matter carbon content calculation model by using unselected logging test data set data, and if the test verification results all meet the first preset condition, the target shale organic matter carbon content calculation model is proved to have reliable precision and can be used for calculating the organic matter carbon content of the same layer of the target shale. If the test and verification result does not meet the first preset condition, step 204 is required to be executed again, and more than S logging test data sets are selected to carry out linear regression analysis fitting again until the test and verification results of the established target shale organic matter carbon content calculation model meet the first preset condition.

It should be further noted that, the first preset condition may be that the ratio between the calculated value of the organic carbon content and the test value of the organic carbon content is smaller than a predetermined threshold value, or that the difference between the calculated value of the organic carbon content and the test value of the organic carbon content is smaller than the predetermined threshold value, which is not specifically intended in the embodiment of the present invention. The smaller the predetermined threshold value is, the higher the accuracy that the target shale organic matter carbon content calculation model needs to meet is, and the higher the accuracy that the established shale organic matter carbon content calculation model calculates the organic matter carbon content is.

In one possible implementation manner, the first preset condition means that each of the N-S logging test data sets that are not selected performs test verification on the target shale organic matter carbon content calculation model, and a difference between the organic matter carbon content calculation value and the organic matter carbon content test value obtained by the test verification result is smaller than 0.5.

Specifically, steps 201 to 205 are described in one embodiment.

The shale of the Zhi-Liu system Loma xi group in the Sichuan basin AAA area is taken as a research object, the data source is Zhu et al 2019 published paper An improved method for evaluating the TOC content of a shale formation using the dual-difference delta log R method, and 91 logging test data sets are shown in Table 2. The first column is a serial number value representing depth, the second column is a clay content value, the third column is an organic carbon content test value, the fourth column is a sound wave time difference value, the fifth column is a compensation intermediate value, the sixth column is a density value, the seventh column is a natural gamma value, and the eighth column is a true formation resistivity logging response value. In addition, in combination with the actual logging response, the shale interval resistivity adjacent to the organic-rich shale interval TOC close to 0, lithographically pure and thick is selected as the clay resistivity Value R _sh The clay resistivity values for the 91 log test data sets were 11ohm m.

Table 2 logging and laboratory analysis data

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Based on the steps 201-205, the logging test data sets with the serial numbers 1-71 are training sets, the logging test data sets with the serial numbers 72-91 are test sets, and a target shale organic matter carbon content calculation model obtained through fitting is expressed as follows:

based on the target shale organic matter carbon content calculation model, testing and verifying the target shale organic matter carbon content calculation model by using a test set, wherein the average absolute error of the organic matter carbon content calculation value and the organic matter carbon content test value is 0.3253, is smaller than 0.5, and accords with a first preset condition. Therefore, the target shale organic matter carbon content calculation model is determined to be a shale organic matter carbon content calculation model.

For the Wang model and Zhao model in the related art, TOC logging calculations were performed using the same training set and test set as the TOC calculation model in the present invention. And comparing the precision of the model of the front person with that of the TOC calculation model in the invention.

For the Wang model, the average absolute error of the test set calculation results is 0.5683; for the Zhao model, the average absolute error of the calculation result of the test set is 0.587, and the average absolute error of the calculation result of the test set of the shale organic matter carbon content calculation model provided by the embodiment of the invention is 0.3253. As can be seen by comparison, the TOC calculation model of the embodiment of the invention has the highest precision.

Step 206: and acquiring a logging test prediction data set of the target shale horizon, wherein the logging test prediction data set comprises a compensation neutron value, a density value, a natural gamma value, a sonic time difference value, a stratum true resistivity logging response value, a clay content value and a clay resistivity value.

It should be noted that after the shale organic matter carbon content calculation model is established, calculation of target shale organic matter carbon content in the same block can be carried out. In the calculation process, the organic carbon content test value of TOC determined by a laboratory experiment method is not required to be acquired, and the calculation time is saved.

Step 207: and determining the organic matter carbon content of the target shale horizon according to the shale organic matter carbon content calculation model and the logging test prediction data set of the target shale horizon.

In the embodiment of the invention, logging test data sets at different depths of a target shale horizon are acquired, clay content logging calculation models for reflecting the mapping relation between a shale clay content index and a clay content value are established according to compensation intermediate sub-values, density values and clay content values in each logging test data set, target organic matter carbon content calculation models are established according to acoustic wave time difference values, formation true resistivity logging response values, clay content values and clay resistivity values in each logging test data set, delta log R values of each logging test data set are determined based on the corrected resistivity-porosity-saturation model and delta log R calculation models, S logging test data sets are selected, and shale content calculation conditions are verified if the target organic matter carbon content calculation models meet the target shale content calculation conditions by using N-S logging test data sets. The basic idea of the method is that shale without organic matters is simplified into a mixture of pores, clay and non-clay minerals, a relation principle of acoustic wave time difference and porosity is established based on a modified resistivity-porosity-saturation model, delta log R values of logging test data sets at different depths are calculated, a plurality of groups of logging test data sets are divided into a training set and a testing set, a target shale organic matter carbon content calculation model is established through the training set, the testing set is combined with a first preset condition for verification, if the shale organic matter carbon content calculation model of a target shale horizon is met, TOC logging calculation is finally carried out on the target shale horizon according to the shale organic matter carbon content calculation model. According to the invention, the traditional theoretical relationship between acoustic wave time difference and resistivity is improved by considering the conductivity of clay and bound water therein and the difference between acoustic wave time difference of clay minerals and other minerals, so that the established shale organic matter carbon content calculation model is more suitable for TOC calculation of shale gas reservoirs with high clay content.

Fig. 3 is a schematic structural diagram of a shale organic matter carbon content calculation model building device provided by the embodiment of the invention. Referring to fig. 3, the apparatus may include:

the first obtaining module 301 is configured to obtain logging test data sets at N different depths of the target shale horizon, where each logging test data set includes a compensated neutron value, a density value, a natural gamma value, a sonic time difference value, a formation true resistivity logging response value, a clay content value, a clay resistivity value, and an organic carbon content test value, and N is a positive integer greater than 10;

the first modeling module 302 is configured to establish a clay content logging calculation model according to the compensated intermediate value, the density value and the clay content value in each logging test data set, where the clay content logging calculation model is a model for reflecting a mapping relationship between the shale clay content index and the clay content value;

a first determining module 303, configured to determine a Δlogr value for each logging test data set based on the corrected resistivity-porosity-saturation model and the Δlogr calculation model according to the acoustic time difference value, the formation resistivity logging response value, the clay content value, and the clay resistivity value in each logging test data set, where the corrected resistivity-porosity-saturation model and the Δlogr calculation model are models based on the acoustic time difference and the resistivity theoretical relationship;

The second modeling module 304 is configured to select S well logging test data sets, and establish a target shale organic carbon content calculation model according to the compensated intermediate value, the density value, the clay content value, the Δlog r value, the natural gamma value, the organic carbon content test value, and the clay content well logging calculation model of each well logging test data set in the S well logging test data sets, where S is a positive integer greater than 1 and less than N;

the verification module 305 is configured to perform test verification on the target shale organic matter carbon content calculation model by using each of the N-S logging test data sets that are not selected, and determine the target shale organic matter carbon content calculation model as a shale organic matter carbon content calculation model of the target shale horizon if the test verification results all meet the first preset condition.

Optionally, the first modeling module includes:

the first determining unit is used for determining the clay content index of each well logging test data set according to the compensation intermediate value and the density value in each well logging test data set;

and the first modeling unit is used for establishing a clay content logging calculation model through a least square method according to the clay content index and the clay content value of each logging test data set.

Optionally, the first determining unit specifically includes:

Optionally, the first modeling unit specifically includes:

Optionally, the first determining module includes:

a second determination unit for determining a corresponding acoustic wave time difference value and a formation true resistivity logging response value in each logging test data set when the constraint condition value is minimum based on the corrected resistivity-porosity-saturation model and the constraint condition based on the acoustic wave time difference value, the formation true resistivity logging response value, the clay content value, and the clay resistivity value in each logging test data set,

Wherein R is _o Logging response value, ohm, for true resistivity of the stratum which does not contain organic matters and has water saturation of 1; v (V) _sh Is the clay content value, v/v; r is R _sh Clay resistivity values, ohm; r is R _w The value of the resistivity of the formation water is 0.1 ohm; Δt is the difference in acoustic wave time, μs/ft; Δt (delta t) _f The difference value is mu s/ft, and the value is 189 mu s/ft; Δt (delta t) _sh The clay sound wave time difference value is mu s/ft, and the value is 89.407 mu s/ft; Δt (delta t) _m The difference value of acoustic wave time of the matrix formed by other minerals of the shale except clay minerals is mu s/ft, and the value is 55 mu s/ft; a is lithology coefficient, and the value is 1; Δt (delta t) _i For any acoustic time difference in the modified resistivity-porosity-saturation model, μs/ft; rt (Rt) _i Logging response values, ohm, for true formation resistivity in the modified resistivity-porosity-saturation model as a function of any sonic time difference;

a fourth determination unit for determining a delta log r value for each well logging test data set based on the difference in acoustic wave time in each well logging test data set, the formation true resistivity logging response value, the clay content value, the clay resistivity value, the target acoustic wave time difference, and the target formation true resistivity logging response value based on a delta log r calculation model,

Wherein R is _t Logging response values for true resistivity of the formation, ohm; r is R _tb1 Logging response values for true resistivity of the target formation, ohm; Δt is the difference in acoustic wave time, μs/ft; Δt (delta t) _b1 Is the target soundWave time difference, μs/ft; m is a cementation index, and the value is 2; v (V) _sh Is the clay content value, v/v; r is R _sh Clay resistivity values, ohm; r is R _w The value of the resistivity of the formation water is 0.1 ohm; Δt (delta t) _f The difference value is mu s/ft, and the value is 189 mu s/ft; Δt (delta t) _sh The clay sound wave time difference value is mu s/ft, and the value is 89.407 mu s/ft; Δt (delta t) _m The difference value of acoustic wave time of the matrix formed by other minerals of the shale except clay minerals is mu s/ft, and the value is 55 mu s/ft; a is a lithology coefficient, and the value is 1.

Optionally, the second modeling module specifically includes:

Optionally, the first preset condition in the verification module means that each of the N-S logging test data sets that are not selected performs test verification on the target shale organic matter carbon content calculation model, and a difference between the organic matter carbon content calculation value and the organic matter carbon content test value obtained by the test verification result is smaller than 0.5.

the second acquisition module is used for acquiring a logging test prediction data set of the target shale horizon, wherein the logging test prediction data set comprises a compensation intermediate value, a density value, a natural gamma value, a sound wave time difference value, a stratum true resistivity logging response value, a clay content value and a clay resistivity value;

It should be noted that: the shale organic matter carbon content calculation model building device provided in the above embodiment only illustrates the division of the above functional modules when building the shale organic matter carbon content calculation model, in practical application, the above functional distribution may be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the device for establishing the shale organic matter carbon content calculation model and the embodiment of the shale organic matter carbon content calculation model establishing method provided in the foregoing embodiments belong to the same concept, and detailed implementation processes of the device are detailed in the method embodiment, which is not described herein again.

Fig. 4 is a schematic structural diagram of a terminal 400 according to an embodiment of the present invention. The terminal 400 may be: a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III, motion picture expert compression standard audio plane 3), an MP4 (Moving Picture Experts Group Audio Layer IV, motion picture expert compression standard audio plane 4) player, a notebook computer, or a desktop computer. The terminal 400 may also be referred to by other names as user equipment, portable terminal, laptop terminal, desktop terminal, etc.

In general, the terminal 400 includes: a processor 401 and a memory 402.

Processor 401 may include one or more processing cores such as a 4-core processor, an 8-core processor, etc. The processor 401 may be implemented in at least one hardware form of DSP (Digital Signal Processing ), FPGA (Field-Programmable Gate Array, field programmable gate array), PLA (Programmable Logic Array ). The processor 401 may also include a main processor, which is a processor for processing data in an awake state, also called a CPU (Central Processing Unit ), and a coprocessor; a coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 401 may integrate a GPU (Graphics Processing Unit, image processor) for rendering and drawing of content required to be displayed by the display screen. In some embodiments, the processor 401 may also include an AI (Artificial Intelligence ) processor for processing computing operations related to machine learning.

Memory 402 may include one or more computer-readable storage media, which may be non-transitory. Memory 402 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 402 is used to store at least one instruction for execution by processor 401 to implement the shale organic matter carbon content calculation model creation method provided by the method embodiments of the present application.

In some embodiments, the terminal 400 may further optionally include: a peripheral interface 403 and at least one peripheral. The processor 401, memory 402, and peripheral interface 403 may be connected by a bus or signal line. The individual peripheral devices may be connected to the peripheral device interface 403 via buses, signal lines or a circuit board. Specifically, the peripheral device includes: at least one of radio frequency circuitry 404, a touch display 404, a camera 406, audio circuitry 407, a positioning component 408, and a power supply 409.

Peripheral interface 403 may be used to connect at least one Input/Output (I/O) related peripheral to processor 401 and memory 402. In some embodiments, processor 401, memory 402, and peripheral interface 403 are integrated on the same chip or circuit board; in some other embodiments, either or both of the processor 401, memory 402, and peripheral interface 403 may be implemented on separate chips or circuit boards, which is not limited in this embodiment.

The Radio Frequency circuit 404 is configured to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuitry 404 communicates with a communication network and other communication devices via electromagnetic signals. The radio frequency circuit 404 converts an electrical signal into an electromagnetic signal for transmission, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 404 includes: antenna systems, RF transceivers, one or more amplifiers, tuners, oscillators, digital signal processors, codec chipsets, subscriber identity module cards, and so forth. The radio frequency circuitry 404 may communicate with other terminals via at least one wireless communication protocol. The wireless communication protocol includes, but is not limited to: metropolitan area networks, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and/or WiFi (Wireless Fidelity ) networks. In some embodiments, the radio frequency circuitry 404 may also include NFC (Near Field Communication ) related circuitry, which is not limiting of the application.

The display 404 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. When the display screen 405 is a touch display screen, the display screen 405 also has the ability to collect touch signals at or above the surface of the display screen 405. The touch signal may be input as a control signal to the processor 401 for processing. At this time, the display screen 405 may also be used to provide virtual buttons and/or a virtual keyboard, also referred to as soft buttons and/or a soft keyboard. In some embodiments, the display 405 may be one, providing a front panel of the terminal 400; in other embodiments, the display 405 may be at least two, and disposed on different surfaces of the terminal 400 or in a folded design; in still other embodiments, the display 405 may be a flexible display disposed on a curved surface or a folded surface of the terminal 400. Even more, the display screen 405 may be arranged in an irregular pattern that is not rectangular, i.e. a shaped screen. The display 405 may be made of LCD (Liquid Crystal Display ), OLED (Organic Light-Emitting Diode) or other materials.

The camera assembly 406 is used to capture images or video. Optionally, camera assembly 406 includes a front camera and a rear camera. Typically, the front camera is disposed on the front panel of the terminal and the rear camera is disposed on the rear surface of the terminal. In some embodiments, the at least two rear cameras are any one of a main camera, a depth camera, a wide-angle camera and a tele camera, so as to realize that the main camera and the depth camera are fused to realize a background blurring function, and the main camera and the wide-angle camera are fused to realize a panoramic shooting and Virtual Reality (VR) shooting function or other fusion shooting functions. In some embodiments, camera assembly 406 may also include a flash. The flash lamp can be a single-color temperature flash lamp or a double-color temperature flash lamp. The dual-color temperature flash lamp refers to a combination of a warm light flash lamp and a cold light flash lamp, and can be used for light compensation under different color temperatures.

The audio circuit 407 may include a microphone and a speaker. The microphone is used for collecting sound waves of users and environments, converting the sound waves into electric signals, and inputting the electric signals to the processor 401 for processing, or inputting the electric signals to the radio frequency circuit 404 for realizing voice communication. For the purpose of stereo acquisition or noise reduction, a plurality of microphones may be respectively disposed at different portions of the terminal 400. The microphone may also be an array microphone or an omni-directional pickup microphone. The speaker is used to convert electrical signals from the processor 401 or the radio frequency circuit 404 into sound waves. The speaker may be a conventional thin film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, not only the electric signal can be converted into a sound wave audible to humans, but also the electric signal can be converted into a sound wave inaudible to humans for ranging and other purposes. In some embodiments, audio circuit 407 may also include a headphone jack.

The location component 408 is used to locate the current geographic location of the terminal 400 to enable navigation or LBS (Location Based Service, location-based services). The positioning component 408 may be a positioning component based on the united states GPS (Global Positioning System ), the beidou system of china, the grainer system of russia, or the galileo system of the european union.

The power supply 409 is used to power the various components in the terminal 400. The power supply 409 may be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When power supply 409 comprises a rechargeable battery, the rechargeable battery may support wired or wireless charging. The rechargeable battery may also be used to support fast charge technology.

In some embodiments, the terminal 400 further includes one or more sensors 410. The one or more sensors 410 include, but are not limited to: acceleration sensor 411, gyroscope sensor 412, pressure sensor 413, fingerprint sensor 414, optical sensor 415, and proximity sensor 416.

The acceleration sensor 411 may detect the magnitudes of accelerations on three coordinate axes of the coordinate system established with the terminal 400. For example, the acceleration sensor 411 may be used to detect components of gravitational acceleration on three coordinate axes. The processor 401 may control the touch display screen 405 to display a user interface in a lateral view or a longitudinal view according to the gravitational acceleration signal acquired by the acceleration sensor 411. The acceleration sensor 411 may also be used for the acquisition of motion data of a game or a user.

The gyro sensor 412 may detect a body direction and a rotation angle of the terminal 400, and the gyro sensor 412 may collect a 3D motion of the user to the terminal 400 in cooperation with the acceleration sensor 411. The processor 401 may implement the following functions according to the data collected by the gyro sensor 412: motion sensing (e.g., changing UI according to a tilting operation by a user), image stabilization at shooting, game control, and inertial navigation.

The pressure sensor 413 may be disposed at a side frame of the terminal 400 and/or at a lower layer of the touch display 405. When the pressure sensor 413 is disposed at a side frame of the terminal 400, a grip signal of the terminal 400 by a user may be detected, and the processor 401 performs a left-right hand recognition or a shortcut operation according to the grip signal collected by the pressure sensor 413. When the pressure sensor 413 is disposed at the lower layer of the touch display screen 405, the processor 401 controls the operability control on the UI interface according to the pressure operation of the user on the touch display screen 405. The operability controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.

The fingerprint sensor 414 is used to collect a fingerprint of the user, and the processor 401 identifies the identity of the user based on the fingerprint collected by the fingerprint sensor 414, or the fingerprint sensor 414 identifies the identity of the user based on the collected fingerprint. Upon recognizing that the user's identity is a trusted identity, the user is authorized by the processor 401 to perform relevant sensitive operations including unlocking the screen, viewing encrypted information, downloading software, paying for and changing settings, etc. The fingerprint sensor 414 may be provided on the front, back or side of the terminal 400. When a physical key or vendor Logo is provided on the terminal 400, the fingerprint sensor 414 may be integrated with the physical key or vendor Logo.

The optical sensor 415 is used to collect the ambient light intensity. In one embodiment, the processor 401 may control the display brightness of the touch display screen 405 according to the ambient light intensity collected by the optical sensor 415. Specifically, when the intensity of the ambient light is high, the display brightness of the touch display screen 405 is turned up; when the ambient light intensity is low, the display brightness of the touch display screen 405 is turned down. In another embodiment, the processor 401 may also dynamically adjust the shooting parameters of the camera assembly 406 according to the ambient light intensity collected by the optical sensor 415.

A proximity sensor 416, also referred to as a distance sensor, is typically provided on the front panel of the terminal 400. The proximity sensor 416 is used to collect the distance between the user and the front of the terminal 400. In one embodiment, when the proximity sensor 416 detects a gradual decrease in the distance between the user and the front face of the terminal 400, the processor 401 controls the touch display 405 to switch from the bright screen state to the off screen state; when the proximity sensor 416 detects that the distance between the user and the front surface of the terminal 400 gradually increases, the processor 401 controls the touch display screen 405 to switch from the off-screen state to the on-screen state.

That is, embodiments of the present invention provide not only a terminal including a processor and a memory for storing instructions executable by the processor, wherein the processor is configured to perform the method of the embodiment shown in fig. 1 or fig. 2, but also a computer-readable storage medium having a computer program stored therein, which when executed by the processor, may implement the shale organic carbon content calculation model establishment method of the embodiment shown in fig. 1 or fig. 2.

Those skilled in the art will appreciate that the structure shown in fig. 4 is not limiting of the terminal 400 and may include more or fewer components than shown, or may combine certain components, or may employ a different arrangement of components.

It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.

Claims

1. The shale organic matter carbon content calculation model establishment method is characterized by comprising the following steps of:

2. The method for modeling shale organic matter carbon content calculation according to claim 1, wherein said modeling clay content logging calculation according to the compensated neutron value, density value, and clay content value in each of said logging test data sets comprises:

3. The method of claim 2, wherein determining the clay content index for each of the well test data sets based on the compensated neutron value and the density value in each of the well test data sets comprises:

wherein I is _c1 Is clay content index, v/v; CNL is the compensation meson value,%; DEN is the density value, g/cm ³ ；DEN _limestone For limestone density, g/cm ³ Take the value of 2.71g/cm ³ ；DEN _f For pore fluid density, g/cm ³ The value is 1g/cm ³ 。

4. The method for building a shale organic matter carbon content calculation model according to claim 2, wherein the building a clay content logging calculation model by a least square method according to the clay content index and the clay content value of each logging test data set comprises:

wherein V is _sh Is the clay content value, v/v; (V) _sh ) _log Calculating the value, v/v, of clay content; i _c1 Is clay content index, v/v; a, a ₁ 、a ₂ Is a fitting parameter.

5. The method of claim 2, wherein determining the Δlogr value for each of the well test data sets based on the corrected resistivity-porosity-saturation model and the Δlogr calculation model based on the acoustic time difference value, the formation true resistivity logging response value, the clay content value, and the clay resistivity value in each of the well test data sets comprises:

wherein R is _o Logging response value, ohm, for true resistivity of the stratum which does not contain organic matters and has water saturation of 1; v (V) _sh Is the clay content value, v/v; r is R _sh Clay resistivity values, ohm; r is R _w The value of the resistivity of the formation water is 0.1 ohm; Δt is the difference in acoustic wave time, μs/ft; Δt (delta t) _f The difference value is mu s/ft, and the value is 189 mu s/ft; Δt (delta t) _sh The clay sound wave time difference value is mu s/ft, and the value is 89.407 mu s/ft; Δt (delta t) _m The difference value of acoustic wave time of the matrix formed by other minerals of the shale except clay minerals is mu s/ft, and the value is 55 mu s/ft; a is lithology coefficient, and the value is 1; Δt (delta t) _i A μ s/ft for any acoustic time difference in the modified resistivity-porosity-saturation model; rt (Rt) _i Logging response values, ohm, for true formation resistivity in the modified resistivity-porosity-saturation model as a function of any sonic time difference;

wherein R is _t Is true resistivity of stratumLogging response values, ohmm; r is R _tb1 Logging response values for true resistivity of the target formation, ohm; Δt is the difference in acoustic wave time, μs/ft; Δt (delta t) _b1 Is the difference value of the target sound wave time, mu s/ft; m is a cementation index, and the value is 2; v (V) _sh Is the clay content value, v/v; r is R _sh Clay resistivity values, ohm; r is R _w The value of the resistivity of the formation water is 0.1 ohm; Δt (delta t) _f The difference value is mu s/ft, and the value is 189 mu s/ft; Δt (delta t) _sh The clay sound wave time difference value is mu s/ft, and the value is 89.407 mu s/ft; Δt (delta t) _m The difference value of acoustic wave time of the matrix formed by other minerals of the shale except clay minerals is mu s/ft, and the value is 55 mu s/ft; a is a lithology coefficient, and the value is 1.

6. The method of claim 1, wherein the establishing a target shale organic carbon content calculation model from the compensated mid-value, the density value, the clay content value, the Δlog r value, the natural gamma value, the organic carbon content test value, and the clay content logging calculation model for each of the S logging test data sets comprises:

7. The method for building a shale organic matter carbon content calculation model according to claim 1, wherein the first preset condition is that each of the N-S logging test data sets that are not selected respectively carries out test verification on the target shale organic matter carbon content calculation model, and a difference between an organic matter carbon content calculation value and the organic matter carbon content test value obtained by test verification results is smaller than 0.5.

8. The method for building a shale organic matter carbon content calculation model according to any one of claims 1-7, wherein after determining the target shale organic matter carbon content calculation model as the shale organic matter carbon content calculation model of the target shale horizon, further comprises:

9. Shale organic matter carbon content calculation model establishment device, its characterized in that, shale organic matter carbon content calculation model establishment device includes:

10. Shale organic matter carbon content calculation model establishment device, characterized in that, the device includes:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to perform the method of any of claims 1-8.