CN110688612B

CN110688612B - Multi-producing-layer oil well yield prediction method based on temperature logging data

Info

Publication number: CN110688612B
Application number: CN201910843143.1A
Authority: CN
Inventors: 年永乐; 郑磊; 程文龙
Original assignee: University of Science and Technology of China USTC
Current assignee: University of Science and Technology of China USTC
Priority date: 2019-09-06
Filing date: 2019-09-06
Publication date: 2021-07-06
Anticipated expiration: 2039-09-06
Also published as: CN110688612A

Abstract

The invention relates to a multi-producing zone oil well yield prediction method based on temperature logging data. Aiming at an oil well with a plurality of production layers, firstly, a comprehensive heat transfer model of the oil well with the plurality of production layers is established by analyzing the heat and mass transfer characteristics of a shaft, a stratum, a reservoir and coupling of the plurality of production layers and the shaft, then, on the basis of the established model, the difference of temperature change rules at different production layers is utilized, actual temperature logging data is substituted into a calculation model to calculate the flow distribution condition in the shaft by an inversion method, and the yield of each production layer is further calculated, and the problem of insufficient prediction precision of a single production layer caused by the adoption of an average distribution method when a traditional well testing method deals with the plurality of production layers is solved by utilizing the method; not only can the total production of the oil well be obtained, but also the production of a single production zone of a multi-production-zone oil well can be accurately predicted.

Description

Multi-producing-layer oil well yield prediction method based on temperature logging data

Technical Field

The invention relates to the technical field of numerical reservoir simulation and temperature well testing, in particular to a method for predicting the yield of a multi-producing-layer oil well.

Background

The yield of oil reservoirs is the most important parameter in petroleum engineering, and the yield is closely related to the actual flowing process of fluids such as oil, water and the like in the production zone of an oil field during production, and most oil wells contain a plurality of production zones. Engineering often determines well production and other reservoir parameters by employing well testing methods. The well testing method is widely applied because the object is data convenient to measure in an oil well, and is not limited by technical conditions in data collection or data analysis, and a plurality of different well testing methods such as a pressure recovery well testing method, a pressure drop well testing method, a pulse well testing method and the like are developed at present.

However, most of oil wells contain a plurality of production zones, and most of the existing well testing methods generally adopt an average distribution method to predict the yield of the oil wells in the plurality of production zones, so that the overall yield can be predicted only, the yield prediction precision of a single production zone is obviously insufficient, and the comprehensive yield prediction effect of the oil wells is further influenced.

Disclosure of Invention

The invention provides a multi-producing zone oil well yield prediction method based on temperature logging data, aiming at the problem that the conventional well testing method is insufficient in single-producing zone productivity prediction precision of a multi-producing zone oil well.

The method comprises the steps of firstly analyzing the heat and mass transfer characteristics of a shaft, a stratum, a reservoir and a multi-zone-shaft coupling, and establishing a heat and mass transfer calculation model of a multi-zone oil well; and calculating the accurate yield of each production layer by using an inversion method based on the actual temperature logging data.

The method for predicting the oil well yield of the multiple producing zones based on the theory comprises the following main steps: (1) firstly, aiming at an oil well needing to be evaluated, collecting various parameters of a stratum, a reservoir, a shaft and injection conditions of the oil well, and providing basic parameters for an established numerical simulation calculation model; (2) collecting actual temperature logging data after the preparation of the calculation model is finished so as to provide a basis for inversion calculation; (3) and finally, giving a value range to the fluid flow of each production layer, and carrying out inversion calculation on the flow of each production layer according to a random approximation inversion method to obtain a prediction result.

The step (1) comprises the following steps: the required basic parameters include, in particular, formation thermal conductivity, specific heat capacity, geothermal gradient, formation density, reservoir permeability, reservoir porosity, well wall parameters, number and perforation positions of primary producing zones, well depth, injection temperature, pressure, and the like.

The step (2) comprises the following steps: the required actual temperature logging data are the temperature values of the fluid in the well and the corresponding depth values.

The step (3) comprises the following steps: the flow rate value range of each production zone can be very rough, then a group of flow rate distribution is randomly generated in the value range according to a random approximate inversion method, the flow rate distribution is substituted into a calculation model to obtain simulated borehole fluid temperature distribution, the simulated borehole fluid temperature distribution is compared with actual temperature logging data to calculate the root mean square error, then the random value of the flow rate is calculated in the next round, all the calculated root mean square error data are compared when the calculation is carried out to the specified round number, and the group with the minimum root mean square error is taken as the optimal prediction result.

The beneficial technical effects of the invention are as follows:

1. the invention solves the problem that the prediction precision of a single production zone is insufficient due to the adoption of an average distribution method when the traditional well testing method is used for dealing with a plurality of production zones.

2. The invention can not only obtain the total yield of the oil well, but also accurately predict the yield of a single production zone of a multi-production-zone oil well.

Drawings

FIG. 1 is a flow chart of the implementation steps of the stochastic approximation inversion method used in the present invention.

Fig. 2(a) is a comparison graph of the inversion result of the bulk temperature distribution value obtained in the embodiment and the actual temperature logging data.

Fig. 2(b) is a comparison graph of the inversion result of the temperature distribution value of the reservoir section obtained in the embodiment and the actual temperature logging data.

Fig. 3 is a reservoir water uptake distribution graph plotted against the flow distribution values of the various producing zones according to an embodiment of the present invention.

Detailed Description

The invention will now be further described by way of example with reference to the accompanying drawings.

The specific implementation mode is as follows: the embodiment provides the prediction of the oil well yield of the multi-producing zone based on the temperature logging data, and the numerical simulation calculation model of the oil well of the multi-producing zone established by the invention and the inversion of the temperature logging data predict the yield of each producing zone.

The numerical simulation calculation model of the multi-producing zone oil well, which is established by the invention, mainly comprises a relatively independent shaft model and a reservoir model which are connected through a flowing and thermal boundary condition, and is used as a stratum heat transfer similar to the boundary condition to calculate by utilizing a one-dimensional radial model and consider unsteady state:

where f (t) is the formation unsteady heat transfer function.

The wellbore model control equation is:

the momentum equation:

energy equation:

wherein a frictional resistance term tau is introduced in consideration of friction between the fluid and the borehole wall_fAnd A is the cross section area of the injection well, the left side of the equation of the energy is the unit depth heat flow of the fluid in the well penetrating through the well wall, and the right side is an unsteady phase and z-direction convection term.

The reservoir model control equation is:

the continuous equation:

wherein S_iIs the saturation degree of the water phase or the oil phase, the sum of the saturation degrees of the water phase and the oil phase is zero,

is the porosity of the porous medium.

The seepage velocity of the fluid in the reservoir during oil production is relatively slow, which can be considered as satisfying Darcy's law of seepage, then the momentum equation can be written as:

wherein K_iFor the permeability of each phase fluidThe permeability of fluids in an actual oilfield reservoir is affected by a number of factors, such as temperature, pressure, saturation, etc., which are set herein for simplicity as a function of the saturation of each phase:

K_i＝K₀*S_i ²

in the heat exchange process, the convective heat transfer between the fluids and the heat conduction between the fluids and the medium are considered, and then the energy equation is:

where the subscript por represents the reservoir porous media.

Aiming at a specific example in the method for predicting the yield of the multi-producing zone, a target oil well is firstly adapted to a numerical simulation calculation model, and parameters such as formation heat conductivity, specific heat capacity, injection temperature and pressure are selected from values given by an actually measured data source and some common engineering parameter values, namely lambda_e＝2.7W/(m·K)；c_p＝900J/(kg·K)；T_in＝273.15K；P_inThe oil well depth is 3500m with 5 main production layers under 11MPa, and the flow rate of each production layer is set to be 0-30 (m) according to the actual measurement conditions³And d), then substituting the actual temperature logging data into the model to calculate according to the inversion calculation flow shown in the figure 1.

Table 1 inversion results of flow distribution

Unit: (m)³/d)	Inversion result 1	Inversion result 2	Inversion result 3	Mean value of
					Reservoir 1 flow	15.95	13.13	12.98	12.89
Reservoir 2 flow	11.97	12.41	12.57	12.61
					Reservoir 3 flow	9.41	9.48	9.75	9.90
Reservoir 4 flow	3.10	3.56	3.47	3.58
					Reservoir 5 flow	4.21	5.10	4.92	4.73
Total flow rate	44.64	43.68	43.69	43.71

Three groups of inversion results obtained according to the situation of the specific embodiment are shown in table 1, and it can be seen from table 1 that the inversion results converge on a flow distribution, and from comparing the temperature distribution inversion results shown in fig. 2 with the actual temperature logging data, it can be seen that the error between the temperature distribution calculation value corresponding to the inversion results and the actual logging data can be kept in a small range.

Claims

1. A multi-producing zone oil well yield prediction method based on temperature logging data is characterized by comprising the following operation steps:

(1) analyzing the heat and mass transfer characteristics of a shaft, a stratum, a reservoir and a multi-zone-shaft coupling, and establishing a heat and mass transfer calculation model of a multi-zone oil well;

the heat and mass transfer calculation model of the multi-zone oil well comprehensively considers the respective calculation models of a shaft, a stratum and a reservoir and the flow-thermal coupling boundary conditions among the calculation models; the shaft is considered to be axial one-dimensional heat transfer and flow and one-dimensional heat transfer between the radial direction and the stratum, and the reservoir is considered to be two-dimensional porous medium two-phase seepage and heat transfer;

the parameters required by the heat and mass transfer calculation model of the multi-zone oil well comprise: the method comprises the following steps of (1) well shaft injection parameters, well wall structure parameters, formation thermophysical parameters, reservoir seepage characteristic parameters and thermophysical parameters;

in the heat and mass transfer calculation model of the multi-production-layer oil well, a co-located grid semi-implicit algorithm is adopted for partial flow thermal coupling calculation of a reservoir;

(2) calculating the accurate yield of each production layer by using an inversion method based on actual temperature logging data;

the inversion calculation method for the flow of each production zone adopts a random approximation method, and comprises the following specific steps:

1) firstly, determining a fuzzy flow value range according to actual working conditions;

2) randomly taking values of the flow of each layer in the range;

3) substituting the random value-taking result into the calculation model to calculate the corresponding wellbore fluid temperature-depth relation;

4) comparing the actual temperature logging data with the calculation data to calculate the root mean square error between the actual temperature logging data and the calculation data and recording the root mean square error;

5) after the steps 2), 3) and 4) are repeated for a certain number of times, comparing the root mean square error calculated for each time;

6) and taking a group of flow values with the minimum root mean square error as an inversion calculation result.