Time-varying phase-controlled random geologic modeling method

Abstract

The invention relates to a time-varying phase-controlled random geologic modeling method, and belongs to the technical field of petroleum and natural gas exploration and development. According to the method, under the constraint of sedimentary facies and lithofacies, the change condition of production data in the production and development process is comprehensively considered, different time-varying stages are reasonably divided according to the change condition of the production data so as to establish a dynamic interpretation model of reservoir parameters of each stage, and a geologic model of each stage is established by adopting a phase-controlled random method; the distribution rule of the remaining oil is further accurately represented, a reliable reference basis is provided for subsequently providing effective potential tapping countermeasures, and the method has important guiding significance for fine development of oil reservoirs.

Description

TECHNICAL FIELD

[0001] The present application relates to a time-varying phased random geology modeling method, and belongs to the technical field of oil and natural gas exploration and development. BACKGROUND

[0002] Most of the old oilfields in China have experienced water flooding development for many years, and the water content has reached more than 90%, entering the late stage of high water content and ultra-high water content development, but the recovery rate is relatively low, with the characteristics of "high water content, low extraction, low water flooding efficiency and large remaining reserves". A considerable amount of remaining oil is still distributed in the reservoir after water flooding in different scales and forms, with huge potential. How to accurately represent the distribution rule of the remaining oil is an important problem.

[0003] In the long-term water flooding development process of sandstone reservoirs, the reservoir properties, pore structure and wettability change continuously, especially after entering the high water content period, under the comprehensive influence of fluid geologic force, gravity differentiation and other factors, water injection is easy to form invalid circulation. The static geology model established in the past cannot reflect the changes in the flow field water flooding process of the reservoir, and the accuracy of the quantitative characterization of the remaining oil is not high. Therefore, it is necessary to establish a set of time-varying dynamic three-dimensional geology model of the reservoir, to simulate the changes of reservoir characteristics in the water flooding process of sandstone reservoirs in stages, to clarify the distribution rule of the remaining oil, and to further put forward effective measures to improve the recovery rate. This has important theoretical value and practical significance for the fine tapping of the remaining oil in sandstone water flooding reservoirs.

[0004] At present, there are many methods for three-dimensional geological modeling at home and abroad, such as the patent document with application number 202110536792.4, which discloses an oil reservoir three-dimensional geological modeling method and device. The method makes full use of seismic, logging, testing and other data, and establishes a three-dimensional geological model of the study area based on the study of geological problems such as stratigraphic framework, structural characteristics and sedimentary characteristics of the study area, including three-dimensional structural model, facies model and physical property parameter model. Finally, according to the needs of oil reservoir numerical simulation research, the three-dimensional geological model is coarsened, so that the established three-dimensional geological model is consistent with the actual situation, and provides reliable data support for the development adjustment and remaining oil prediction of oilfields. In the paper "Application Research of Facies-controlled Random Modeling in Fine Description of Reservoirs" published by Zheng Lihui, Xing Yuzhong, et al. in the Journal of Southwest Petroleum University, Vol. 29, No. 6, December 2007, the spatial distribution of reservoir parameters is constrained by the research results of sedimentary microfacies, and the normal transformation of reservoir parameters is obtained. On this basis, the random simulation of reservoir properties is carried out, and the facies-controlled random model is established. In 2012, Bin Shan published "Geological Spatiotemporal Data Modeling Technology and Its Application in Oil Reservoir Development" in Oil and Gas Industry. The theory and algorithm of spatiotemporal data modeling are analyzed. In view of the problems of large amount of data operation, complex information change matrix and long training time in spatiotemporal modeling, two network models of fractional multi-aggregation neural network and Legendre orthogonal basis transformation multi-aggregation neural network are designed. Through the analysis and processing of the display image, the reservoir is visually displayed, which provides intuitive help for oilfield workers.

[0005] In summary, the current method can establish a corresponding three-dimensional geological model, but the model is generally static. However, oilfields have generally experienced long-term water injection development, and reservoir parameters (porosity, permeability, pore structure, etc.) will change with development. The established model regards the porosity and permeability of the underground reservoir as unchangeable, and regards the reservoir as a static factor to establish a static geological model. Although this model can represent the geological conditions, the static geological model established cannot correctly reflect the physical property change of the formation, which further affects the development effect of the subsequent formation. SUMMARY

[0006] The purpose of the present application is to provide a time-varying facies-controlled random geological modeling method to solve the problem that the currently established static geological model cannot accurately represent the formation physical property change in different stages of water injection development, resulting in poor formation development effect.

[0007] To solve the above technical problems, the present application provides a time-varying facies-controlled random geological modeling method, which comprises the following steps:

[0008] 1) using the production data changes of each well in the target zone reservoir to divide the reservoir into multiple stages, wherein the production data is the reservoir development curve;

[0009] 2) using the reservoir parameters and logging data in each stage, and under the constraint of sedimentary facies and lithofacies, establishing a corresponding geological model for each stage.

[0010] Further, the reservoir parameters in steps 1) and 2) are porosity and permeability, and the logging data includes acoustic travel time, induced conductivity, and natural gamma.

[0011] Further, the reservoir development curve in step 1) includes the time of reservoir production development, water cut curve and water injection curve, and the reservoir is divided into stages according to the changes of water cut and water injection during the reservoir development process.

[0012] Further, the establishment process of the geological model of each stage in step 2) is as follows:

[0013] The logging data of each well in the target zone reservoir is controlled by sedimentary microfacies and lithofacies to establish a sedimentary microfacies model;

[0014] According to the logging data and reservoir parameters of each stage, a reservoir parameter dynamic interpretation model is established for the corresponding stage;

[0015] Under the constraint of the established sedimentary microfacies model, a physical property parameter time-varying dynamic model of each stage is established based on the reservoir parameter dynamic interpretation model.

[0016] Further, the establishment process of the sedimentary microfacies model is as follows:

[0017] Obtain the core data of each well in the target zone reservoir;

[0018] Determine the sedimentary microfacies of each well in the target zone reservoir according to the lithofacies and the core data;

[0019] Determine the sedimentary microfacies distribution of the target zone reservoir using the characteristics of various microfacies and the sedimentary microfacies of each well, to obtain the sedimentary microfacies model of the target zone reservoir.

[0020] Further, after establishing the reservoir parameter dynamic interpretation model of each stage, the physical property time-varying characteristics of different facies belts of each well in the target reservoir are obtained according to the reservoir parameter variation trend of different facies belts, different periods and different water cut conditions.

[0021] Further, the stages divided in step 1) include the preliminary development stage, the perfect adjustment and expansion capacity stage, the comprehensive adjustment stage and the comprehensive treatment stage.

[0022] The beneficial effects of the present application are: as an improved application, the present application comprehensively considers the change of production data in the production development process under the constraint of sedimentary facies and lithofacies, reasonably divides different time-varying stages according to the change of production data, establishes a dynamic interpretation model of reservoir parameters in each stage, and further accurately represents the distribution rule of remaining oil by using facies-controlled random method to establish a geological model in each stage, thereby providing reliable reference basis for subsequent effective potential tapping countermeasures, and having important guiding significance for fine development of the oil reservoir. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 is a flow chart of the time-varying facies-controlled random geological modeling method of the present application;

[0024] Figure 2 is a dynamic knowledge base of the Sha 2+3 reservoir in the west region of A oilfield established in the embodiment of the present application;

[0025] Figure 3 is a comprehensive columnar chart of P2-396 core of the Sha 2+3 reservoir in the west region of A oilfield established in the embodiment of the present application;

[0026] Figure 4 is a sedimentary microfacies distribution map of the Sha 2+3 reservoir in the west region of A oilfield established in the embodiment of the present application;

[0027] Figure 5 is a trend interface (after standardization) of interval transit time of the 2-6 layer of the Sha 2+3 reservoir in the west region of A oilfield established in the embodiment of the present application;

[0028] Figure 6 is a time-varying feature comparison profile of the P2-515 well and the P2-515h well of the Sha 2+3 reservoir in the west region of A oilfield established in the embodiment of the present application;

[0029] Figure 7 is a time-varying feature statistical chart of interval transit time of different facies belts of all wells of the Sha 2+3 reservoir in the west region of A oilfield established in the embodiment of the present application;

[0030] Figure 8 is a time-varying dynamic modeling stage division schematic diagram of the Sha 2+3 reservoir in the west region of A oilfield established in the embodiment of the present application;

[0031] Figure 9 is a sedimentary microfacies data analysis of the Sha 2+3 reservoir in the west region of A oilfield established in the embodiment of the present application;

[0032] Figure 10 is a lithofacies data analysis of the Sha 2+3 reservoir in the west region of A oilfield established in the embodiment of the present application;

[0033] Figure 11 is a structural model of the Sha 2+3 reservoir in the west region of A oilfield established in the embodiment of the present application;

[0034] Figure 12 is a sedimentary microfacies model of the Es2 2+3 reservoir in the west region of A oilfield in the embodiment of the present application;

[0035] Figure 13 is a lithofacies model of the Es2 2+3 reservoir in the west region of A oilfield in the embodiment of the present application;

[0036] Figure 14 is a property grade quantization table of the Es2 2+3 reservoir in the west region of A oilfield in the embodiment of the present application;

[0037] Figure 15 is a porosity-permeability interpretation model of the Es2 2+3 reservoir in the west region of A oilfield in the embodiment of the present application;

[0038] Figure 16a is a statistical chart of time-varying characteristics of the property of the channel facies belt of all wells in the Es2 2+3 reservoir in the west region of A oilfield in the embodiment of the present application;

[0039] Figure 16b is a statistical chart of time-varying characteristics of the property of the front edge sand facies belt of all wells in the Es2 2+3 reservoir in the west region of A oilfield in the embodiment of the present application;

[0040] Figure 16c is a statistical chart of time-varying characteristics of the property of the far sand facies belt of all wells in the Es2 2+3 reservoir in the west region of A oilfield in the embodiment of the present application;

[0041] Figure 17 is a comparison chart of the time-varying facies-controlled random geologic model and the original static three-dimensional geologic model of the Es2 2+3 reservoir in the west region of A oilfield in the embodiment of the present application;

[0042] Figure 18 is a comparison chart of the history matching of the conventional model and the time-varying model of the Es2 2+3 reservoir in the west region of A oilfield in the embodiment of the present application. DETAILED DESCRIPTION

[0043] The specific embodiments of the present application are further described below with reference to the accompanying drawings.

[0044] The present application is aimed at the case that reservoir characteristics change with the stage of water injection development, and divides the reservoir into multiple stages by using the production data change of each well in the target reservoir, and the production data is the reservoir development curve; and establishes the corresponding geological model of each stage under the constraint of sedimentary facies and lithofacies by using the reservoir parameters and logging data in each stage. The present application comprehensively considers the change in the production development process under the constraint of sedimentary facies and lithofacies, reasonably divides different time-varying stages according to the change of production data, establishes the dynamic interpretation model of reservoir parameters in each stage, and establishes the geological model of each stage by using the facies-controlled random method, to further accurately represent the distribution rule of remaining oil, provides reliable reference basis for subsequent effective potential tapping countermeasures, and has important guiding significance for fine development of the reservoir.

[0045] The present application is further described below by taking the time-varying facies-controlled random geological modeling of the Sha 2+3 reservoir in the west region of A oilfield as an example.

[0046] The Sha 2+3 reservoir in the west region of A oilfield belongs to delta front deposition, has strong hydrodynamic force, diversified sedimentary structure, and good reservoir physical property. The block has experienced water injection development for nearly 40 years, and the reservoir physical property has changed greatly with the development of the reservoir. The traditional static geological model does not consider the time variation of the reservoir physical property, so that the prediction of remaining oil is inaccurate. The dynamic modeling method of the present application can improve the accuracy of the prediction of remaining oil, and the implementation process of the method is as shown in Figure 1 , and the specific implementation steps are described in detail below.

[0047] 1. Obtain various data of the target block, including logging data, layered data, structure interpretation, core data, sedimentary microfacies, indoor analysis test data, and production dynamic monitoring data to establish a reservoir knowledge base.

[0048] This embodiment collects and collates the logging data, layered data, core data, sedimentary data, seismic interpretation data, indoor analysis test data, and production dynamic data of the Sha 2+3 reservoir in the west region of A oilfield to establish the dynamic knowledge base of the Sha 2+3 reservoir in the west region of A oilfield, as shown in Figure 2 .

[0049] 2. According to the reservoir knowledge base established in step 1, prepare the sedimentary microfacies equivalent map to determine the sedimentary microfacies distribution.

[0050] This embodiment prepares the core comprehensive columnar chart based on the core observation and particle size analysis according to the dynamic knowledge base of the Sha 2+3 reservoir in the west region of A oilfield established in step 1, as shown in Figure 3 , and further divides the sedimentary microfacies of 412 wells of the Sha 2+3 reservoir in the west region of A oilfield in combination with the lithofacies, summarizes and analyzes the characteristics of various microfacies, and prepares the sedimentary microfacies distribution of the Sha 2+3 reservoir in the west region of A oilfield, as shown inFigure 4 The sedimentary microfacies distribution is determined in this way.

[0051] 3. Under the constraints of steps 1 and 2, a standardization study of the well logging curves is performed to determine the time-varying characteristics of the well logging data of acoustic time difference, induced conductance, natural gamma, porosity and permeability.

[0052] In this embodiment, a standardization study of the well logging curves of 412 wells in the A oilfield west zone Es2 upper 2+3 reservoir is performed to determine the time-varying characteristics of the well logging data of acoustic time difference, induced conductance, natural gamma, porosity and permeability.

[0053] 1) The well logging data is standardized. Since the acoustic time difference (AC) is sensitive to the response of reservoir properties, it is also commonly used for well logging data for interpreting macroscopic physical property parameters such as porosity and permeability. Therefore, the time-varying characteristics of the acoustic time difference of the A oilfield west zone Es2 upper 2+3 reservoir are focused on in this study. The standardization of the acoustic time difference of the A oilfield west zone Es2 upper 2+3 reservoir is performed by selecting a relatively thick mudstone layer at the bottom of the Es2 upper 2-6 sublayer as a standard layer (mudstone thickness is stable, borehole is relatively regular, and there is no large collapse), and using the trend surface method to standardize 412 wells in the whole area, and preparing an acoustic time difference trend interface graph of the Es2 upper 2-6 sublayer of the A oilfield west zone Es2 upper 2+3 reservoir, as shown in Figure 5 .

[0054] 2) The well logging data of the A oilfield west zone Es2 upper 2+3 reservoir is counted, and the acoustic time difference, induced conductance, natural gamma and porosity and permeability curves of the A oilfield west zone Es2 upper 2+3 reservoir are compared and analyzed to analyze the time-varying characteristics of the A oilfield west zone Es2 upper 2+3 reservoir. ① An A oilfield west zone Es2 upper 2+3 reservoir well correlation profile is established, as shown in Figure 6 , it can be seen from the well logging characteristics of well P2-515 and well P2-515h that the acoustic time difference and natural gamma of good reservoirs gradually increase with the extension of the water injection time. ② The acoustic time difference of all wells in the A oilfield west zone Es2 upper 2+3 reservoir is counted, and a time-varying characteristic statistical graph of the acoustic time difference of each facies belt with time is established, as shown in Figure 7 , it can be seen that compared with the channel facies (SH), the physical properties of the channel flanking facies (Q) are slightly worse, and the acoustic time difference is slightly lower. The acoustic time difference and the natural gamma value slightly increase with the water injection time during the water injection development process. ③ The physical properties of the sheet sand (Y) are relatively the worst of the first two types of sedimentary facies. The acoustic time difference value of the reservoir during the water injection development process has a tendency to decrease with the extension of the water injection time, but the decrease is relatively slow. The overall well logging time-varying characteristics show a "Matthew effect".

[0055] 4. Develop the development curves of the study area, including the time of production development, water cut curve, injection curve, liquid production curve and oil production curve, and reasonably divide the dynamic geological modeling stages, divide the whole process of the oilfield from the beginning of development to the present into n stages.

[0056] In this embodiment, the development curves of the Sha 2+3 reservoir in the west area of A oilfield are prepared, including the time of production development, water cut curve, injection curve, liquid production curve and oil production curve of the Sha 2+3 reservoir in the west area of A oilfield. According to the obvious changes of reservoir water cut and injection with development time, for example, at the beginning, the water cut and injection are both small, and with development, the water cut and injection both have rapid growth, among which the water cut tends to be stable after a certain stage of growth, while the injection increases first and has certain fluctuations in the growth process, and after growing to a certain extent, the injection decreases. The above changes of water cut and injection can be used for stage division. As other embodiments, when using the water cut curve and the injection curve for stage division, the liquid production curve and the oil production curve can also be used as auxiliary conditions. In this embodiment, the dynamic geological modeling stages of the Sha 2+3 reservoir in the west area of A oilfield are reasonably divided according to the above method, which are divided into four time-varying dynamic geological modeling stages, as shown in Figure 8 : ① preliminary development stage (1981-1983); ② perfect adjustment and expansion of production capacity stage (1984-1987); ③ comprehensive adjustment stage (1988-1991); ④ comprehensive treatment stage (1992-present). Among them, in the preliminary development stage, the water cut and injection both have relatively large growth; in the perfect adjustment and expansion of production capacity stage, the injection has relatively large growth, and the water cut grows slowly; in the comprehensive adjustment stage, the water cut is unchanged, and the injection is in a fluctuating state; in the comprehensive treatment stage, the water cut is still unchanged, and the injection is in a decreasing state due to the use of other development methods. As other embodiments, more stages can also be divided on this basis, for example, n is 5 or 6.

[0057] 5. Input the data of all wells into the three-dimensional geological modeling software, use sedimentary microfacies and lithofacies multi-level control, and sequentially establish the structure model, the lithofacies model and the sedimentary microfacies model.

[0058] In this embodiment, the stratification, well deviation, logging data, structure data and sedimentary microfacies of more than 400 wells of the Sha 2+3 reservoir in the west area of A oilfield are input into the three-dimensional geological modeling software, the structure model of the Sha 2+3 reservoir in the west area of A oilfield is established first (as shown in Figure 11 ), the sedimentary microfacies data (as shown in Figure 9 ) and the lithofacies data (as shown in Figure 10 ) of the Sha 2+3 reservoir in the west area of A oilfield are comprehensively analyzed, and the sedimentary microfacies model (as shown in Figure 12 ) and the lithofacies model (as shown in Figure 13 ) are established respectively.

[0059] 6. Establishing reservoir porosity dynamic interpretation model and reservoir permeability dynamic interpretation model in stages to clarify the time-varying characteristics of reservoir physical property parameters in the process of water injection development.

[0060] In this embodiment, under the constraints of sedimentary microfacies and lithofacies of the Es2 2+3 reservoir in the west area of A oilfield, according to the four time-varying dynamic stages divided, the corresponding porosity, permeability and logging data of each stage are selected to establish the reservoir parameter dynamic interpretation model of the Es2 2+3 reservoir in the west area of A oilfield, as shown in Figure 15 On this basis, the good, medium and poor reservoir properties are quantitatively subdivided from qualitative, and the physical property grade quantification table of the Es2 2+3 reservoir in the west area of A oilfield is established, as shown in Figure 14 Then, according to the variation trend of porosity and permeability of different facies (such as channel facies, front sand facies and far sand facies), different periods and different water content, the time-varying characteristic statistical diagram of all wells of the Es2 2+3 reservoir in the west area of A oilfield in different facies is metamorphosed, as shown in Figure 16a Figure 16b Figure 16c Figure 16a is the time-varying characteristic statistical diagram of channel facies, Figure 16b is the time-varying characteristic statistical diagram of front sand facies, Figure 16c is the time-varying characteristic statistical diagram of far sand facies, which lays a foundation for the next step of time-varying facies-controlled random geological modeling of the Es2 2+3 reservoir in the west area of A oilfield.

[0061] 1) The porosity of the reservoir with good original physical property (such as channel facies) does not change obviously, and basically remains unchanged in the process of water injection development, and the permeability gradually increases with the extension of water injection time, with an average increase of about 1.5 times.

[0062] 2) The porosity of the reservoir with medium physical property (such as front sand facies) does not change obviously with the extension of water injection time, and the permeability also slightly increases in the process of water injection development, with a small increase.

[0063] 3) The permeability of the reservoir with relatively poor five-star facies (such as far sand facies) shows a slight decreasing trend with the extension of water injection time, and the change range of porosity is not large.

[0064] 7. Combined with the dynamic geological modeling stages divided in step 4, and based on the time-varying dynamic interpretation model of reservoir parameters of the Es2 2+3 reservoir in the west area of A oilfield established in step 6, the sequential indicator simulation method is optimized to establish the time-varying facies-controlled random geological models of porosity, permeability and shale content of the 2+3 reservoir in the west area of A oilfield from the first stage to the fourth stage, and compared with the original static three-dimensional geological model, it can be obviously seen that the reservoir properties of different stages change with the long-term scouring of water injection, as shown in​​​Figure 17 As shown in the figure. From the time-varying phased random geologic modeling of each stage, it can be seen that both porosity and permeability are increasing with long-term water drive development. Through the comparison of daily oil production and comprehensive water cut history matching of A oilfield west area Sha 2+3 reservoir production scale model and time-varying model, as shown in the figure, it can be seen that the history matching rate is greatly improved from 78% to 92.54%, which provides reliable data support for the accurate tapping of remaining oil. Figure 18 As shown in the figure. From the time-varying phased random geologic modeling of each stage, it can be seen that both porosity and permeability are increasing with long-term water drive development. Through the comparison of daily oil production and comprehensive water cut history matching of A oilfield west area Sha 2+3 reservoir production scale model and time-varying model, as shown in the figure, it can be seen that the history matching rate is greatly improved from 78% to 92.54%, which provides reliable data support for the accurate tapping of remaining oil.

Claims

1. A time-varying phase-controlled stochastic geological modeling method, characterized in that, The method includes the following steps: 1) The reservoir is divided into multiple stages by utilizing the changes in production data of each well in the target area. The production data is the reservoir development curve. 2) Using reservoir parameters and logging data from each stage, establish a geological model corresponding to each stage under the constraints of sedimentary facies and lithofacies.

2. The time-varying phase-controlled stochastic geological modeling method according to claim 1, characterized in that, The reservoir parameters in steps 1) and 2) are porosity and permeability, and the logging data includes sonic transit time, induced conductivity, and natural gamma.

3. The time-varying phase-controlled stochastic geological modeling method according to claim 1, characterized in that, The reservoir development curve in step 1) includes the reservoir production and development time, water cut curve and water injection curve. The reservoir is divided into stages according to the changes in water cut and water injection during the reservoir development process.

4. The time-varying phase-controlled stochastic geological modeling method according to claim 1, characterized in that, The process of establishing the geological model at each stage in step 2) is as follows: The logging data of each well in the target reservoir were analyzed using multi-level control of sedimentary microfacies and lithofacies to establish a sedimentary microfacies model. Establish dynamic interpretation models of reservoir parameters for each stage based on logging data and reservoir parameters at each stage; Under the constraints of the established sedimentary microfacies model, and based on the dynamic interpretation model of reservoir parameters, a time-varying dynamic model of physical property parameters for each stage is established.

5. The time-varying phase-controlled stochastic geological modeling method according to claim 4, characterized in that, The process of establishing a sedimentary microfacies model: Obtain core data from each well in the target reservoir area; The sedimentary microfacies of each well in the target area reservoir were determined based on the lithofacies and the core data. By utilizing the characteristics of various microfacies and the sedimentary microfacies of each well, the distribution of sedimentary microfacies in the target area reservoir is determined, thereby obtaining the sedimentary microfacies model of the target area reservoir.

6. The time-varying phase-controlled stochastic geological modeling method according to claim 4, characterized in that, After establishing dynamic interpretation models of reservoir parameters at each stage, the time-varying characteristics of physical properties of different facies zones in each well of the target reservoir are obtained based on the changing trends of reservoir parameters in different facies zones, different periods, and different water cut conditions.

7. The time-varying phase-controlled stochastic geological modeling method according to claim 1, characterized in that, The stages in step 1) include the initial development stage, the improvement, adjustment and capacity expansion stage, the comprehensive adjustment stage, and the comprehensive management stage.

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