CN114169600A - Reservoir oil content prediction model establishing method, prediction method and prediction device - Google Patents

Abstract

The invention provides a reservoir oil content prediction model establishing method, a prediction method and a device, wherein the method comprises the following steps: the method comprises the steps of analyzing core physical property data and single-well monthly production data of historical exploitation wells in a work area, determining a functional relation between a production coefficient and a low water-cut oil recovery period of a reservoir in the work area, a corresponding relation between a target flow unit and well logging data, and a corresponding relation between the target flow unit and the low water-cut oil recovery period, thereby determining a functional relation between the production coefficient and the well logging data in a preset oil content calculation formula, further obtaining a reservoir oil content prediction model.

Description

Reservoir oil content prediction model establishing method, prediction method and prediction device

Technical Field

The invention belongs to the field of oil and gas development, and particularly relates to a reservoir oil content prediction model establishing method, a prediction method and a prediction device.

Background

With the development of a large number of oil fields, the evaluation work of a water flooded layer becomes important day by day, and logging information can provide continuous physical property and oil-containing data of a development interval, so that the method is an important means for evaluating the water flooded layer. In the low-permeability pore type water flooded layer, because cracks do not develop, communicated interparticle pores are main channels for flowing of oil water in the development process, however, factors influencing the oil content of a development well are many, and the oil content of the development well is influenced by reservoir heterogeneity, the viscosity of the oil, the property of injected water, the injection-production relation, the well pattern arrangement and the development duration.

The physical analysis data of the core and the open hole well logging curve are static data reflecting the characteristics of the oil reservoir before development. After the oil field is put into water flooding development, factors influencing the production dynamics of a development well are many, and a method for representing the flooding dynamics of a reservoir stratum is difficult to establish by only combining a logging curve with dynamic production data. At present, no universally applicable and accurate prediction method exists for the change situation of the oil content of a low-permeability pore type reservoir after flooding.

Disclosure of Invention

In view of the foregoing problems in the prior art, an object of the present disclosure is to provide a reservoir oil content prediction model establishing method, a reservoir oil content prediction model predicting method, and a reservoir oil content prediction model predicting device, which can improve the accuracy of reservoir oil content prediction.

In order to solve the technical problems, the specific technical scheme is as follows:

in one aspect, provided herein is a method of modeling a reservoir oil cut prediction, the method comprising:

obtaining core physical property data, logging data and monthly production data of a target layer in a historical exploitation well in a work area;

according to the core physical property data, performing flow unit division on a target layer position in the work area to obtain a plurality of flow units;

determining a production coefficient corresponding to each historical production well in the work area according to the monthly production data of the single well of the historical production well;

determining a target flow unit in each historical production well according to the unit thickness of each flow unit and the low water content oil recovery period of each historical production well, and determining the corresponding relation between the target flow unit and the logging data and the corresponding relation between the target flow unit and the low water content oil recovery period;

fitting to obtain a functional relation between the production coefficient and the low water content oil recovery period according to the production coefficient corresponding to each historical production well and the low water content oil recovery period of each historical production well;

and obtaining a reservoir oil content prediction model according to the corresponding relation between the target flow unit and the logging data, the corresponding relation between the target flow unit and the low water content oil recovery period, the functional relation between the production coefficient and the low water content oil recovery period and a preset oil content calculation formula.

Further, the core property data includes porosity and permeability;

according to the core physical property data, dividing a target layer position in the working area into a plurality of flow units, and obtaining a plurality of flow units, wherein the flow units comprise:

calculating and obtaining a flow zone index of a target position in each historical exploitation well according to the porosity and the permeability;

fitting to obtain a distribution probability cumulative curve of the flow band index;

dividing the flow band index into a plurality of unit types according to the slope change of the distribution probability accumulation curve;

and dividing the target horizon in each historical production well into a plurality of flow units according to the unit types.

Further, the determining a target flow cell in each historical production well based on the cell thickness of each flow cell and the low water recovery period of each historical production well comprises:

calculating and obtaining the thickness ratio of each flow unit in each historical production well according to the unit thickness of each flow unit;

fitting to obtain the corresponding relation between the thickness ratio of the same type of flow units in different historical production wells and the low water content oil extraction period and corresponding fitting coefficients;

and taking the flow unit corresponding to the unit type in the corresponding relation with the highest fitting coefficient as a target flow unit.

Further, the logging data comprises a natural gamma curve and a sound wave time difference curve;

determining a correspondence between the target flow unit and the well logging data, comprising:

acquiring logging data of the position of a target flow unit in each historical production well;

calculating and obtaining the shale content of a target flowing unit in each historical exploitation well according to a natural gamma curve in the logging data;

calculating and obtaining the effective porosity of the target flow unit in each historical exploitation well according to the shale content and the acoustic time difference curve in the logging data;

and counting effective porosity and sound wave time difference curves of the target flow unit in all historical production wells, acquiring an effective porosity range and a sound wave time difference value range, and establishing a corresponding relation between a data set formed by the effective porosity range and the sound wave time difference value range and the target flow unit.

Further, the production coefficients include a time coefficient and a flow coefficient;

and fitting to obtain a functional relation between the production coefficient and the low water content oil recovery period according to the production coefficient corresponding to each historical production well and the low water content oil recovery period of each historical production well, wherein the functional relation comprises the following steps:

fitting to obtain a functional relation between the time coefficient and the low water content oil extraction period;

fitting to obtain a functional relation between the flow coefficient and the low water content oil recovery period.

Further, the obtaining a reservoir oil content prediction model according to the corresponding relationship between the target flow unit and the logging data, the corresponding relationship between the target flow unit and the low water content oil recovery period, the functional relationship between the production coefficient and the low water content oil recovery period, and a preset oil content calculation formula includes:

determining a calculation function of the low water content oil recovery period relative to the logging data according to the corresponding relation between the target flow unit and the logging data and the corresponding relation between the target flow unit and the low water content oil recovery period;

determining a calculation function of the production coefficient relative to the logging data according to a calculation function of the low water content oil recovery period relative to the logging data and a function relation of the production coefficient and the low water content oil recovery period;

and substituting the calculation function of the production coefficient relative to the logging data into a preset oil content calculation formula to obtain a reservoir oil content prediction model.

Further, the preset oil content calculation model formula is represented by the following formula:

wherein f is_oOil content for monthly average production; both alpha and k are production coefficients, a is a time coefficient, and k is a flow coefficient; t is t_DIn order to have a dimensionless time,

q_infor injection rate, t is the real time, V_pIs the injection volume.

In another aspect, provided herein is a method for predicting the oil content of a reservoir, the method comprising:

acquiring real-time production data and logging data of a reservoir to be detected, wherein the production data comprises injection speed, real time and injection volume;

and bringing the real-time production data and the logging data into a reservoir oil content prediction model established by the method, and predicting to obtain the real-time oil content in the reservoir to be tested.

In another aspect, there is also provided a reservoir oil cut prediction model building apparatus, the apparatus comprising:

the data acquisition module is used for acquiring core physical property data, logging data and monthly single well production data of a target horizon in a historical production well in a work area;

the flow unit dividing module is used for dividing a target horizon in the work area into a plurality of flow units according to the core physical property data;

the production coefficient determining module is used for determining the production coefficient corresponding to each historical production well in the work area according to the monthly production data of the single well of the historical production well;

the target flow unit generation module is used for determining a target flow unit in each historical production well according to the unit thickness of each flow unit and the low water content oil recovery period of each historical production well, and determining the corresponding relation between the target flow unit and the logging data and the corresponding relation between the target flow unit and the low water content oil recovery period;

the fitting module is used for fitting to obtain a functional relation between the production coefficient and the low water content oil recovery period according to the production coefficient corresponding to each historical production well and the low water content oil recovery period of each historical production well;

and the model establishing module is used for obtaining a reservoir oil content prediction model according to the corresponding relation between the target flow unit and the logging data, the corresponding relation between the target flow unit and the low water content oil recovery period, the functional relation between the production coefficient and the low water content oil recovery period and a preset oil content calculation formula.

In another aspect, a computer device is also provided herein, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the method as described above when executing the computer program.

By adopting the technical scheme, the reservoir oil content prediction model establishing method, the prediction method and the device, by analyzing the core physical property data and the monthly production data of a single well of the historical production wells in the work area, the functional relationship between the production coefficient of the reservoir in the work area and the low water content oil extraction period is determined, and the corresponding relation between the target flow unit and the logging data and the corresponding relation between the target flow unit and the low water content oil recovery period, thereby determining the functional relation between the production coefficient and the logging data in the preset oil content calculation formula and further obtaining a reservoir oil content prediction model, when the oil content of other logs is predicted, the oil content of the reservoir to be measured can be accurately determined only by determining the logging data, the prediction accuracy of the oil content of the reservoir can be improved, and the method has important significance for describing single-well flooding dynamics and determining a water-flooding front edge.

In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

In order to more clearly illustrate the embodiments or technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 shows a schematic representation of an implementation environment for a method provided by embodiments herein;

FIG. 2 is a schematic diagram illustrating steps of a reservoir oil cut prediction model building method provided in an embodiment herein;

FIG. 3 shows a schematic diagram of the flow cell dividing step in an embodiment herein;

FIG. 4 is a schematic diagram illustrating the relation between the average water cut per month of production and the month of production in one embodiment herein;

FIG. 5 is a schematic diagram illustrating the flow cell partitioning results in one embodiment herein;

FIG. 6 is a graph illustrating target flow cell thickness fraction and low water cut oil recovery period fit results in one embodiment herein;

FIG. 7 is a schematic diagram illustrating the steps for determining the correspondence between the well log data and the target flow cell in the embodiments herein;

FIG. 8 is a diagram illustrating the partitioning results of different flow cell partitioning schemes in an embodiment herein;

FIG. 9 shows a comparison of fitted oil content versus actual monthly average oil content during flooding of an X1 well in an embodiment herein;

FIG. 10 is a graph showing the results of a time coefficient and period of low water recovery fit in one embodiment herein;

FIG. 11 is a schematic diagram illustrating the reservoir oil cut prediction modeling steps in the examples herein;

FIG. 12 is a schematic structural diagram illustrating a reservoir oil content prediction model building apparatus provided in an embodiment of the present disclosure;

FIG. 13 is a schematic diagram illustrating steps of a method for reservoir oil content prediction provided in embodiments herein;

fig. 14 is a schematic structural diagram illustrating a reservoir oil content prediction apparatus provided in an embodiment of the present disclosure;

fig. 15 shows a schematic structural diagram of a computer device provided in an embodiment herein.

Description of the symbols of the drawings:

10. a client;

20. a server;

30. a network;

100. a data acquisition module;

200. a flow cell dividing module;

300. a production coefficient determination module;

400. a target flow cell generation module;

500. a fitting module;

600. a model building module;

700. a reservoir data acquisition module to be tested;

800. a prediction module;

1502. a computer device;

1504. a processor;

1506. a memory;

1508. a drive mechanism;

1510. an input/output module;

1512. an input device;

1514. an output device;

1516. a presentation device;

1518. a graphical user interface;

1520. a network interface;

1522. a communication link;

1524. a communication bus.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments herein without making any creative effort, shall fall within the scope of protection.

It should be noted that the terms "first," "second," and the like in the description and claims herein and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments herein described are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, apparatus, article, or device that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or device.

In the prior art, the oil content of the oil produced in the development process of an oil field development well is generally predicted through the physical property analysis data of a rock core and the static data such as a logging curve of a barefoot well, but the method is difficult to accurately analyze the change condition of the oil content after the low-permeability porous reservoir is flooded, so that the oil and gas exploitation work of a subsequent development well is influenced.

In order to solve the above problems, embodiments of the present specification provide a method for establishing a reservoir oil content prediction model, and by using a model established by the method, prediction of an oil content in a reservoir can be accurately and reliably achieved. As shown in fig. 1, the implementation environment of the method is schematically illustrated, and the method may include a client 10 and a server 20, where the client 10 and the server 20 are connected through a network 30, and data interaction may be implemented through the network 30.

The client 10 is configured to obtain data of a target horizon in a historical production set in a work area, where the data may include core property data, logging data, and monthly single well production data, and send the data to the server 20 through the network 30, the server 20 establishes a reservoir oil content prediction model according to a preset model establishment method, and specifically, by analyzing the core property data and monthly single well production data of the historical production wells in the work area, determines a functional relationship between a production coefficient and a low water content oil recovery period of the reservoir in the work area, a corresponding relationship between a target flow unit and the logging data, and a corresponding relationship between the target flow unit and the low water content oil recovery period, thereby determining a functional relationship between the production coefficient and the logging data in a preset oil content calculation formula, and further obtaining a reservoir oil content prediction model, and when predicting the oil content of other logs, the oil content of the reservoir to be detected can be accurately determined only by determining the logging data, and the accuracy of reservoir oil content prediction can be improved.

In an optional embodiment, the server 20 may be an independent physical server, may also be a server cluster or a distributed system formed by a plurality of physical servers, and may also be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a CDN (Content Delivery Network), a big data and artificial intelligence platform, and the like.

In an alternative embodiment, client 10 may build a reservoir oil-content prediction model in conjunction with server 20. In particular, the client 10 may include, but is not limited to, smart phones, desktop computers, tablet computers, laptop computers, smart speakers, digital assistants, Augmented Reality (AR)/Virtual Reality (VR) devices, smart wearable devices, and other types of electronic devices. Optionally, the operating system running on the electronic device may include, but is not limited to, an android system, an IOS system, Linux, Windows, and the like.

In addition, it should be noted that fig. 1 shows only one application environment provided by the present disclosure, and in practical applications, other application environments may also be included, for example, the establishment of a reservoir oil content prediction model may also be implemented on the client 10.

In particular, the embodiment provides a reservoir oil content prediction model building method, and by using the model built by the method, the prediction of the oil content in the reservoir can be accurately and reliably realized. Fig. 2 is a schematic diagram of steps of a method for establishing a reservoir oil content prediction model provided in an embodiment herein, and the present specification provides the method operation steps as described in the embodiment or the flowchart, but more or less operation steps can be included based on conventional or non-inventive labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual system or apparatus product executes, it can execute sequentially or in parallel according to the method shown in the embodiment or the figures. Specifically, as shown in fig. 2, the method may include:

s101: obtaining core physical property data, logging data and monthly production data of a target layer in a historical exploitation well in a work area;

s102: according to the core physical property data, performing flow unit division on a target layer position in the work area to obtain a plurality of flow units;

s103: determining a production coefficient corresponding to each historical production well in the work area according to the monthly production data of the single well of the historical production well;

s104: determining a target flow unit in each historical production well according to the unit thickness of each flow unit and the low water content oil recovery period of each historical production well, and determining the corresponding relation between the target flow unit and the logging data and the corresponding relation between the target flow unit and the low water content oil recovery period;

s105: fitting to obtain a functional relation between the production coefficient and the low water content oil recovery period according to the production coefficient corresponding to each historical production well and the low water content oil recovery period of each historical production well;

s106: and obtaining a reservoir oil content prediction model according to the corresponding relation between the target flow unit and the logging data, the corresponding relation between the target flow unit and the low water content oil recovery period, the functional relation between the production coefficient and the low water content oil recovery period and a preset oil content calculation formula.

It can be understood that in the embodiment of the description, the physical property data of the core in the historical exploitation well in the work area, the logging data and the single-well monthly production data are analyzed to obtain the oil content of the reservoir used for predicting other logs in the work area, so that the accuracy of oil content prediction is improved, meanwhile, the dynamic evaluation of the oil content in the production process can be realized, and the adaptability and the application range of the model are improved.

It should be noted that, in the embodiments of the present specification, the work area may be a low-permeability pore type water flooded layer, and since cracks do not develop, the connected inter-granular pores are the main channels for oil and water flow during the development process, so that the accuracy of predicting the oil content of the low-permeability reservoir may be improved by analyzing the pore structure in the reservoir.

The target horizon may be a particular reservoir location in the formation of the work area, such as a three-stacked extended length 6 reservoir.

In embodiments herein, the core property data may include porosity and permeability; correspondingly, as shown in fig. 3, the dividing a target horizon in the work area into a plurality of flow units according to the core physical property data to obtain a plurality of flow units includes:

s201: calculating and obtaining a flow zone index of a target position in each historical exploitation well according to the porosity and the permeability;

s202: fitting to obtain a distribution probability cumulative curve of the flow band index;

s203: dividing the flow band index into a plurality of unit types according to the slope change of the distribution probability accumulation curve;

s204: and dividing the target horizon in each historical production well into a plurality of flow units according to the unit types.

The Flow Zone Index (FZI) can reflect the pore structure characteristics of a reservoir, flow units can be divided by the distribution probability of the flow zone index, and the calculation process of the flow zone index is as follows:

step 1.1: calculating to obtain a reservoir quality index according to the porosity and the permeability of the reservoir sampling core; the calculation formula is as follows:

wherein RQI is the reservoir quality index, K is the permeability, and the unit is mD; phi is the gas porosity, phi is decimal.

Step 2.1: calculating to obtain a flowing zone index according to the reservoir quality index and the permeability of the reservoir sampling core; the calculation formula is as follows:

wherein FZI is a flow band index.

In the same historical development well, as the core sampling can be carried out at different positions of a target layer (for example, different sampling points are arranged in the thickness direction), the pore structure characteristics of the reservoir in the target layer in each historical development well can be determined through the flow zone indexes, and then the flow units can be divided according to the flow zone indexes.

On the basis of obtaining a plurality of flowing zone indexes of a target horizon in each historical development well, fitting to obtain a distribution probability accumulation curve of the flowing zone indexes through a statistical analysis method, and dividing the target horizon into different unit types according to the slope change condition of the curve.

The slope change of the curve can be a position with larger slope change and can be a unit type division point, for example, a slope change range can be set, the slopes between two adjacent coordinate points are sequentially calculated, the difference value between the current slope and the previous slope is calculated, and when the difference value is not in the slope change range, any coordinate point in the current slope is used as the unit type division point, so that the unit type can be divided based on the slope change, the fact that reservoirs with the same or similar pore structure properties are divided into the same type of flow units is guaranteed, and the accuracy of flow unit division is improved.

Illustratively, taking a certain well group of ultra-low permeability X-well regions developed by water flooding of Ordos basin as an example, the development horizon is a three-system extended group length 6 reservoir, and production and water flooding development are started in 1997. And acquiring conventional logging curves, core physical property analysis porosity, permeability and monthly production data of the well and the well group in which the well is positioned, and carrying out standardized processing on the logging curves. Logging curves, monthly average water production rate data of the oil wells and production months can be collected from all wells in the research area. In part of wells, data of porosity and permeability of core physical property analysis are collected at the research horizon. When the oil field is developed by water injection, the monthly water content of the production well is very low and is in the waterless oil production period. X well zone X1 well mean water cut per month of production versus month of production as shown in figure 4, so that periods of low water recovery (i.e. periods or months of recovery with reservoir water content below 20%) in the well can be determined.

Reservoir quality indexes RQI and flow zone indexes FZI in different development wells in a research area can be obtained through the formula (1) and the formula (2), distribution probability cumulative drawing is carried out on the flow zone indexes, and then the slope change of a curve is obtained through fitting, so that a three-section line graph shown in figure 5 can be obtained, namely, a target horizon is divided into 3 types of flow units, namely three types of flow units I, II and III, and the target horizon in each historical development well can be divided into three flow units.

In an embodiment of the present description, the determining a target flow cell in each historical production well based on the cell thickness of each flow cell and the low water recovery period of each historical production well comprises:

calculating and obtaining the thickness ratio of each flow unit in each historical production well according to the unit thickness of each flow unit;

fitting to obtain the corresponding relation between the thickness ratio of the same type of flow units in different historical production wells and the low water content oil extraction period and corresponding fitting coefficients;

and taking the flow unit corresponding to the unit type in the corresponding relation with the highest fitting coefficient as a target flow unit.

It will be appreciated that the pore characteristics of the different flow cells are different, and by analysing the relationship between the different flow cells and the water cut during production, the water content or oil content of the same flow unit in other positions in the same work area can be effectively predicted through the flow unit, and in the same work area (research area), due to the characteristic of continuous extension of the stratum, reservoirs with the same pore characteristics have certain continuity or correlation on other lithological characteristics, such as the thickness of the reservoirs, the reservoir thickness ratio of the same type of flow units may have certain consistency, thus by establishing a correspondence between the thickness fraction of the different types of flow cells and the periods of low water recovery, therefore, the type of the flow unit with the functional relation with the low water content oil extraction period is determined, and the reliability and the accuracy of the subsequent prediction model establishment are ensured.

In embodiments of the present description, the thickness fraction of the flow cell may be a proportion of the thickness of the different flow cells in the historical development well and the total thickness of the target horizon in the historical development well.

Illustratively, a certain well group of the ultra-low permeability X well region developed by water injection in the Ordos basin is taken as an example for explanation, three flow units can be obtained through the unit type division, according to the unit type division method, the long 6 reservoir of 5 production wells in the well group of the certain well of the X well region is subjected to flow unit division, the proportion of the thickness of different flow units of each well in the total thickness of the reservoir and the low water content oil extraction period with the monthly water content of less than 20% are counted, and the results are shown in the following table 1.

TABLE 1 thickness ratio of different flow cell for each well and low water content oil recovery period in the research area

Through the steps, the functional relationship between the three types of flow units and the low water content oil recovery period and the corresponding fitting coefficients are sequentially fitted, so that a good linear relationship between the thickness ratio of the 3 (i.e. III) type flow unit and the low water content oil recovery period can be obtained, as shown in fig. 6, the functional relationship schematic diagram obtained by fitting between the thickness ratio of the 3 type flow unit and the low water content oil recovery period (or duration) is obtained, and therefore the prediction of the low water content oil recovery period can be carried out by utilizing the thickness ratio of the 3 type flow unit.

In an embodiment of the present specification, the well log data includes a natural gamma curve and an acoustic time difference curve; accordingly, as shown in fig. 7, determining the corresponding relationship between the target flow unit and the well logging data includes:

s301: acquiring logging data of the position of a target flow unit in each historical production well;

s302: calculating and obtaining the shale content of a target flowing unit in each historical exploitation well according to a natural gamma curve in the logging data;

s303: calculating and obtaining the effective porosity of the target flow unit in each historical exploitation well according to the shale content and the acoustic time difference curve in the logging data;

s304: and counting effective porosity and sound wave time difference curves of the target flow unit in all historical production wells, acquiring an effective porosity range and a sound wave time difference value range, and establishing a corresponding relation between a data set formed by the effective porosity range and the sound wave time difference value range and the target flow unit.

It can be understood that, in the actual exploitation process, sampling and exploitation efficiencies of a core sample are low, and therefore, in order to improve prediction of an oil content in a reservoir to be tested or an exploitation well, prediction of the oil content in the exploitation well can be performed through logging data, specifically, a corresponding relation between historical logging data in a historical exploitation well and a determined target flow unit is determined through the logging data of the exploitation well to be tested, and then the target flow unit in the exploitation well to be tested is determined through the logging data of the exploitation well to be tested in the same work area, and then a corresponding prediction process is performed, so that the efficiency of determining the target flow unit in the reservoir to be tested or the exploitation well to be tested is improved, and further the accuracy of oil content prediction is improved.

The well log data may also include natural potentials and well diameter curves, etc.

Optionally, the shale content of the target flow cell in the historical production well is calculated by the following formula:

wherein, V_shIs the mud content, GR is the natural gamma curve, GR_minIs the natural gamma curve minimum, GR_maxIs the natural gamma curve maximum.

The effective pore space of the target flow unit in the historical production well is calculated by the following formula:

wherein Por is the effective porosity of the reservoir, Δ t_maAcoustic time difference, Δ t, for sandstone frameworks_fAcoustic time difference, Δ t, for pore fluids_shIs the sound wave time difference of the pure mudstone section, delta t is the sound wave time difference curve, V_shIs the argillaceous content.

The effective porosity range in the target flow unit in all the historical production wells can be obtained through the formula (3) and the formula (4), the acoustic time difference curve range in the target flow unit in all the historical production wells can also be determined according to the acoustic time difference curve and the position of the target flow unit, and then the corresponding relation between the data set formed by the effective porosity range and the acoustic time difference value range and the target flow unit can be established. That is to say, for the production well to be tested, effective porosities and acoustic wave time difference curves at different positions can be obtained through calculation of logging data, and the position of the target flow unit in the target layer position in the production well to be tested can be determined according to the corresponding relation between the established data set consisting of the effective porosity range and the acoustic wave time difference value range and the target flow unit, so that the efficiency of determining the target flow unit in the reservoir layer to be tested or the production well is improved.

Of course, the logging data of other types of flow units may also be processed to obtain corresponding effective porosity and acoustic wave time difference, so that the characteristics of different types of flow units can be visually distinguished, for example, as shown in table 2 below, the classification conditions of different flow units based on the logging data are shown.

TABLE 2 partitioning conditions for different flow cells based on well log data

In actual work, any historical exploitation well in an X-scenic spot can be selected as comparison for detection, the flow unit is divided based on the core physical property data, the flow unit is divided based on the logging curve, and the two division modes are compared, as shown in fig. 8, the comparison result shows that the flow unit division result based on the logging curve and the flow unit division result based on the core analysis have good consistency.

In the examples of the present specification, the production coefficients include a time coefficient and a flow coefficient. Correspondingly, the fitting according to the production coefficient corresponding to each historical production well and the low water content oil recovery period of each historical production well to obtain the functional relationship between the production coefficient and the low water content oil recovery period comprises:

fitting to obtain a functional relation between the time coefficient and the low water content oil extraction period;

fitting to obtain a functional relation between the flow coefficient and the low water content oil recovery period.

It can be understood that the production coefficient is a parameter affecting the oil content in the production process of the production well, specifically, may be a time coefficient and a flow coefficient, and may be obtained by fitting monthly production data of a single well in a historical production well, and the specific fitting process is a means commonly used in the field of oil and gas production, for example, fitting by a preset fitting program through Matlab programming, and the specific fitting process is not limited in the embodiments of the present specification.

The single-well monthly production data can comprise water content, oil content, yield, reservoir heterogeneity, crude oil viscosity, injected water property, injection-production relation, well pattern arrangement, development duration and the like, and different fitting parameters are selected according to fitting requirements.

Through the steps, the target flow unit in the development well to be tested can be determined through the logging data, then the corresponding low water content oil recovery period is determined on the basis of establishing the corresponding relation between the target flow unit and the low water content oil recovery period, and the production coefficient of the target horizon in the development well to be tested is calculated through the production coefficient (i.e. time coefficient and flow coefficient) obtained through fitting and the functional relation of the low water content oil recovery period, so that the production coefficient in the development well to be tested can be calculated through the corresponding characteristics (such as thickness ratio) of the target flow unit, and the oil content in the development well to be tested can be conveniently predicted.

Illustratively, by fitting the production data of the different producing wells of the X-well zone, the dimensionless time transformation coefficient (C:, and:, and the dimensional time conversion coefficients of the factors of the dimensional time on the factors of the dimensional time transformation of the production rates of the production wells, and production of the production wells, and production of the well types, and production wells, and production of the well types, and production rates of the well types, and production rates of the well types, and production wells, and production rates of the well types, and production wells, and production rates of the well types, and production rates of the well types, and production wells, and well types of the well types, and well types

Wherein q is_inFor injection rate, V_pInjection volume) is set to 0.008, the actual month number is converted into dimensionless time, the single well month production data in the water injection development process of 5 production wells in the research area are fitted, the time coefficient alpha and the single well flow coefficient k are determined, and the single well fitting coefficient R is calculated²And well areaAnd (3) averaging the flow coefficient k to obtain the result shown in the following table 3, wherein the oil content prediction model production coefficient (namely the flooding dynamic model parameter) and the fitting effect of each well reservoir in the research area are obtained.

TABLE 3 prediction model production coefficient and fitting Effect for oil content of reservoir of each well in research area

As can be seen from Table 3, the oil content historical data fitting effect based on the method provided by the embodiment of the specification is remarkable, and the time coefficient and the low water content oil recovery period have a strong linear relation. As shown in fig. 9, the fitted oil content during the flooding of the X1 well is compared with the actual monthly average oil content. As shown in fig. 10, it can be determined that the correlation coefficient between the time coefficient and the low water content oil recovery period reaches 0.987, the correlation is strong, the functional relationship between the time coefficient and the low water content oil recovery period can be obtained by fitting, the flow coefficient and the low water content oil recovery period have no obvious linear relationship, that is, the variation range of the flow coefficient in the same work area is small, and the flow coefficient can be determined as a fixed value, for example, the average value of the flow coefficient in the historical production well in the work area.

In this embodiment of the present specification, as shown in fig. 11, the obtaining a reservoir oil content prediction model according to the corresponding relationship between the target flow unit and the log data, the corresponding relationship between the target flow unit and the low water content oil recovery period, the functional relationship between the production coefficient and the low water content oil recovery period, and a preset oil content calculation formula includes:

s401: determining a calculation function of the low water content oil recovery period relative to the logging data according to the corresponding relation between the target flow unit and the logging data and the corresponding relation between the target flow unit and the low water content oil recovery period;

s402: determining a calculation function of the production coefficient relative to the logging data according to a calculation function of the low water content oil recovery period relative to the logging data and a function relation of the production coefficient and the low water content oil recovery period;

s403: and substituting the calculation function of the production coefficient relative to the logging data into a preset oil content calculation formula to obtain a reservoir oil content prediction model.

It can be understood that, in the embodiment of the present specification, the corresponding well logging data in the target flow unit is determined through analysis of a historical production well in a work area, and then the corresponding relationship (or calculation function) between the low water content oil recovery period and the well logging data is obtained according to the corresponding relationship between the target flow unit and the low water content oil recovery period.

Alternatively, the preset oil content calculation module formula is represented by the following formula:

wherein f is_oOil content for monthly average production; both alpha and k are production coefficients, a is a time coefficient, and k is a flow coefficient; t is t_DIn order to have a dimensionless time,

q_infor injection rate, t is the real time, V_pIs the injection volume.

The method for establishing the reservoir oil content prediction model provided by the embodiment of the specification determines the functional relationship between the production coefficient and the low water content oil recovery period of the reservoir in the work area, the corresponding relationship between the target flow unit and the logging data, and the corresponding relationship between the target flow unit and the low water content oil recovery period by analyzing the core physical property data, the logging data and the single-well monthly production data of the historical exploitation wells in the work area, thereby determining the functional relationship between the production coefficient and the logging data in the preset oil content calculation formula, further obtaining the reservoir oil content prediction model, and when predicting the oil content of other logs, only determining the oil content of the reservoir to be tested according to the logging data, and improving the accuracy of reservoir oil content prediction.

Based on the same inventive concept, the embodiment of the present specification further provides a device for establishing a reservoir oil content prediction model, as shown in fig. 12, the device includes:

the data acquisition module 100 is used for acquiring core physical property data, logging data and monthly single well production data of a target horizon in a historical production well in a work area;

the flow unit dividing module 200 is configured to perform flow unit division on a target horizon in the work area according to the core physical property data to obtain a plurality of flow units;

the production coefficient determining module 300 is configured to determine a production coefficient corresponding to each historical production well in the work area according to monthly production data of the single well of the historical production well;

a target flow unit generation module 400, configured to determine a target flow unit in each historical production well according to the unit thickness of each flow unit and the low water content oil recovery period of each historical production well, and determine a corresponding relationship between the target flow unit and the log data, and a corresponding relationship between the target flow unit and the low water content oil recovery period;

the fitting module 500 is used for fitting to obtain a functional relation between the production coefficient and the low water content oil recovery period according to the production coefficient corresponding to each historical production well and the low water content oil recovery period of each historical production well;

the model establishing module 600 is configured to obtain a reservoir oil content prediction model according to the corresponding relationship between the target flow unit and the logging data, the corresponding relationship between the target flow unit and the low water content oil recovery period, the functional relationship between the production coefficient and the low water content oil recovery period, and a preset oil content calculation formula.

The advantages obtained by the device are consistent with those obtained by the method, and the embodiments of the present description are not repeated.

On the basis of the method for establishing the reservoir oil content prediction model provided above, an embodiment of the present specification further provides a reservoir oil content prediction method, as shown in fig. 13, where the method includes:

s501: acquiring real-time production data and logging data of a reservoir to be detected, wherein the production data comprises injection speed, real time and injection volume;

s502: and bringing the real-time production data and the logging data into a reservoir oil content prediction model established by the method, and predicting to obtain the real-time oil content in the reservoir to be tested.

It can be understood that the step is the application of the method for establishing the model, and it should be noted that the historical exploitation wells related in the establishment process of the reservoir oil content prediction model and the exploitation wells corresponding to the reservoir to be tested belong to the same work area, so that the similarity of the reservoir pore structure and the reservoir lithology characteristics in the same work area can be ensured, and the accuracy of oil content prediction is ensured.

In the actual prediction process, the corresponding relation between logging data and a target flow unit in the reservoir oil content prediction model and the corresponding relation between the thickness ratio of the target flow unit and the low water content oil extraction period can be firstly determined, so that the effective porosity range and the sound wave time difference range in the target flow unit are obtained, the logging data in the reservoir to be detected are processed to obtain the effective porosity and the sound wave time difference at different positions, the region falling in the effective porosity range and the sound wave time difference range is determined as the target flow unit, so that the thickness ratio of the target flow unit can be obtained, and the low water content oil extraction period of the reservoir to be detected is determined by combining the corresponding relation between the thickness ratio of the target flow unit and the low water content oil extraction period; and then, calculating to obtain a production coefficient according to a functional relation between the production coefficient in the reservoir oil content prediction model and the low water content oil extraction period, and bringing the calculated production coefficient and the real-time production data of the reservoir to be tested into the formula (5) to predict the oil content of the reservoir to be tested.

Accordingly, there is also provided herein a reservoir oil content prediction apparatus, as shown in fig. 14, comprising:

the reservoir data acquisition module 700 to be detected is used for acquiring real-time production data and logging data of a reservoir to be detected, wherein the production data comprises injection speed, real time and injection volume;

and the prediction module 800 is used for bringing the real-time production data and the logging data into a reservoir oil content prediction model established by the method for the reservoir to be tested, and predicting to obtain the real-time oil content in the reservoir to be tested.

As shown in fig. 15, a computer device provided for embodiments herein, an apparatus herein may be a computer device in the embodiments herein to perform the methods herein described above, and the computer device 1502 may include one or more processors 1504, such as one or more Central Processing Units (CPUs), each of which may implement one or more hardware threads. The computer device 1502 may also include any memory 1506 for storing any kind of information, such as code, settings, data, etc. For example, and without limitation, the memory 1506 may include any one or more of the following in combination: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any memory may use any technology to store information. Further, any memory may provide volatile or non-volatile retention of information. Further, any memory may represent fixed or removable components of the computer device 1502. In one case, when the processor 1504 executes associated instructions stored in any memory or combination of memories, the computer device 1502 can perform any of the operations of the associated instructions. The computer device 1502 also includes one or more drive mechanisms 1508, such as a hard disk drive mechanism, an optical drive mechanism, and the like, for interacting with any of the memories.

The computer device 1502 may also include an input/output module 1510(I/O) for receiving various inputs (via input device 1512) and for providing various outputs (via output device 1514)). One particular output mechanism may include a presentation device 1516 and an associated Graphical User Interface (GUI) 1518. In other embodiments, input/output module 1510(I/O), input device 1512, and output device 1514 may also be excluded, as just one computer device in a network. The computer device 1502 may also include one or more network interfaces 1520 for exchanging data with other devices via one or more communication links 1522. One or more communication buses 1524 couple the above-described components together.

Communication link 1522 may be implemented in any manner, such as over a local area network, a wide area network (e.g., the Internet), a point-to-point connection, and the like, or any combination thereof. The communication link 1522 may comprise any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

Corresponding to the method shown in any one of fig. 2, fig. 3, fig. 7, fig. 11 and fig. 13, the present embodiments also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the above-mentioned method.

Embodiments herein also provide computer readable instructions, wherein the program, when executed by a processor, causes the processor to perform a method as shown in any one of fig. 2, fig. 3, fig. 7, fig. 11 and fig. 13.

It should be understood that, in various embodiments herein, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments herein.

It should also be understood that, in the embodiments herein, the term "and/or" is only one kind of association relation describing an associated object, meaning that three kinds of relations may exist. For example, a and/or B, may represent: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.

Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the several embodiments provided herein, it should be understood that the disclosed system, apparatus, and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments herein.

In addition, functional units in the embodiments herein may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present invention may be implemented in a form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

The principles and embodiments of this document are explained herein using specific examples, which are presented only to aid in understanding the methods and their core concepts; meanwhile, for the general technical personnel in the field, according to the idea of this document, there may be changes in the concrete implementation and the application scope, in summary, this description should not be understood as the limitation of this document.

Claims (10)

1. A reservoir oil content prediction model building method is characterized by comprising the following steps:

obtaining core physical property data, logging data and monthly production data of a target layer in a historical exploitation well in a work area;

according to the core physical property data, performing flow unit division on a target layer position in the work area to obtain a plurality of flow units;

determining a production coefficient corresponding to each historical production well in the work area according to the monthly production data of the single well of the historical production well;

determining a target flow unit in each historical production well according to the unit thickness of each flow unit and the low water content oil recovery period of each historical production well, and determining the corresponding relation between the target flow unit and the logging data and the corresponding relation between the target flow unit and the low water content oil recovery period;

fitting to obtain a functional relation between the production coefficient and the low water content oil recovery period according to the production coefficient corresponding to each historical production well and the low water content oil recovery period of each historical production well;

and obtaining a reservoir oil content prediction model according to the corresponding relation between the target flow unit and the logging data, the corresponding relation between the target flow unit and the low water content oil recovery period, the functional relation between the production coefficient and the low water content oil recovery period and a preset oil content calculation formula.

2. The method of claim 1, wherein the core property data comprises porosity and permeability;

according to the core physical property data, dividing a target layer position in the working area into a plurality of flow units, and obtaining a plurality of flow units, wherein the flow units comprise:

calculating and obtaining a flow zone index of a target position in each historical exploitation well according to the porosity and the permeability;

fitting to obtain a distribution probability cumulative curve of the flow band index;

dividing the flow band index into a plurality of unit types according to the slope change of the distribution probability accumulation curve;

and dividing the target horizon in each historical production well into a plurality of flow units according to the unit types.

3. The method of claim 1, wherein determining the target flow cell in each historical production well based on the cell thickness of each flow cell and the period of low water recovery for each historical production well comprises:

calculating and obtaining the thickness ratio of each flow unit in each historical production well according to the unit thickness of each flow unit;

fitting to obtain the corresponding relation between the thickness ratio of the same type of flow units in different historical production wells and the low water content oil extraction period and corresponding fitting coefficients;

and taking the flow unit corresponding to the unit type in the corresponding relation with the highest fitting coefficient as a target flow unit.

4. The method of claim 1, wherein the well log data comprises a natural gamma curve and an acoustic time difference curve;

determining a correspondence between the target flow unit and the well logging data, comprising:

acquiring logging data of the position of a target flow unit in each historical production well;

calculating and obtaining the shale content of a target flowing unit in each historical exploitation well according to a natural gamma curve in the logging data;

calculating and obtaining the effective porosity of the target flow unit in each historical exploitation well according to the shale content and the acoustic time difference curve in the logging data;

and counting effective porosity and sound wave time difference curves of the target flow unit in all historical production wells, acquiring an effective porosity range and a sound wave time difference value range, and establishing a corresponding relation between a data set formed by the effective porosity range and the sound wave time difference value range and the target flow unit.

5. The method of claim 1, wherein the production coefficients include a time coefficient and a flow coefficient;

and fitting to obtain a functional relation between the production coefficient and the low water content oil recovery period according to the production coefficient corresponding to each historical production well and the low water content oil recovery period of each historical production well, wherein the functional relation comprises the following steps:

fitting to obtain a functional relation between the time coefficient and the low water content oil extraction period;

fitting to obtain a functional relation between the flow coefficient and the low water content oil recovery period.

6. The method of claim 1, wherein obtaining a reservoir oil content prediction model according to the correspondence between the target flow unit and the log data, the correspondence between the target flow unit and the low water content oil recovery period, the functional relationship between the production coefficient and the low water content oil recovery period, and a preset oil content calculation formula comprises:

determining a calculation function of the low water content oil recovery period relative to the logging data according to the corresponding relation between the target flow unit and the logging data and the corresponding relation between the target flow unit and the low water content oil recovery period;

determining a calculation function of the production coefficient relative to the logging data according to a calculation function of the low water content oil recovery period relative to the logging data and a function relation of the production coefficient and the low water content oil recovery period;

and substituting the calculation function of the production coefficient relative to the logging data into a preset oil content calculation formula to obtain a reservoir oil content prediction model.

7. The method of claim 1, wherein the predetermined oil content calculation model is expressed by the following formula:

wherein f is_oOil content for monthly average production; both alpha and k are production coefficients, alpha is a time coefficient, and k is a flow coefficient; t is t_DIn order to have a dimensionless time,

q_infor injection rate, t is the real time, V_pIs the injection volume.

8. A method for predicting the oil cut of a reservoir, the method comprising:

acquiring real-time production data and logging data of a reservoir to be detected, wherein the production data comprises injection speed, real time and injection volume;

and bringing the real-time production data and the logging data into a reservoir oil content prediction model established by the method of any one of claims 1 to 7, and predicting to obtain the real-time oil content in the reservoir to be tested.

9. An apparatus for modeling a reservoir oil cut prediction, the apparatus comprising:

the data acquisition module is used for acquiring core physical property data, logging data and monthly single well production data of a target horizon in a historical production well in a work area;

the flow unit dividing module is used for dividing a target horizon in the work area into a plurality of flow units according to the core physical property data;

the production coefficient determining module is used for determining the production coefficient corresponding to each historical production well in the work area according to the monthly production data of the single well of the historical production well;

the target flow unit generation module is used for determining a target flow unit in each historical production well according to the unit thickness of each flow unit and the low water content oil recovery period of each historical production well, and determining the corresponding relation between the target flow unit and the logging data and the corresponding relation between the target flow unit and the low water content oil recovery period;

the fitting module is used for fitting to obtain a functional relation between the production coefficient and the low water content oil recovery period according to the production coefficient corresponding to each historical production well and the low water content oil recovery period of each historical production well;

and the model establishing module is used for obtaining a reservoir oil content prediction model according to the corresponding relation between the target flow unit and the logging data, the corresponding relation between the target flow unit and the low water content oil recovery period, the functional relation between the production coefficient and the low water content oil recovery period and a preset oil content calculation formula.

10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 7 or 8 when executing the computer program.

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