Fine geological modeling and development potential analysis method

Abstract

The invention discloses a fine geological modeling and development potential analysis method, which comprises the following steps of: S1, evaluating a gas advantage enrichment area by adopting a method of combining fuzzy mathematics and analytic hierarchy process to obtain an evaluation value; s2, geological modeling and numerical simulation research optimization are carried out, residual gas reserve prediction is carried out according to the evaluation value, and a prediction result is obtained; and S3, according to a prediction result, economic benefit-oriented layered residual gas potential tapping and well pattern optimization research is carried out. According to the fine geological modeling and development potential analysis method, the problem that the prior art cannot accurately describe the dominant gas-bearing reservoir, so that the optimal selection of the drilling position is not accurate enough is solved.

Description

Technical Field

[0001] This invention belongs to the field of reservoir development potential analysis technology, specifically involving refined geological modeling and development potential analysis methods. Background Technology

[0002] The Mizhi Gas Field is located in the eastern part of the Ordos Basin. The main producing layer is the Shan 2 Member, with the He 8 Member being the secondary main producing layer. The He 8 Member is characterized by a broad, gentle slope dipping southwest and rising northeast, with a slope less than 5°. A relatively gentle plateau exists in the middle, and a series of low-amplitude "nose-like" structures perpendicular to the slope exist in the NE direction. The average burial depth of the reservoir is 2284–2334 m, with a small effective thickness of individual layers, generally 3.1–3.5 m. The reservoir is highly heterogeneous, belonging to low-porosity and low-permeability reservoirs, with porosity generally ranging from 6% to 10%, averaging 6.85%; permeability is generally (0.15–0.7) × 10⁻⁶. −3 μm 2 The average value is 0.29 × 10⁻⁶. −3 μm 2 It belongs to a sandstone reservoir with extremely low permeability, which leads to generally low production and rapid decline in the output of individual wells.

[0003] The main source rocks in the Mizhi area are the second member of the Shanxi Formation, the Taiyuan Formation, and the Benxi Formation of the Upper Paleozoic, exhibiting a "widely covered" distribution. These source rocks interact with sand bodies, and in the overlying He8 member adjacent to the source rocks, a lower-source, upper-reservoir gas reservoir can form. Overall, the Mizhi gas field is rich in resources and has great development potential. The He8 member, with its relatively high permeability sand bodies, is the dominant reservoir. Tight sandstone or mudstone can form effective caprocks and lateral sealing layers, combining to form lithological traps or physical property traps of the dominant reservoir. When the source rocks generate gas, they create overpressure, which vertically injects into the He8 member through fractures, forming a gas reservoir. During injection, reservoirs with relatively better physical properties are preferentially injected. Influenced by the degree of injection and gas-water gravity differentiation, the dominant sand bodies in the structural platform area and the "nose-shaped" structural area have higher gas abundance. In the absence of sufficient gas supply, the formation of gas reservoirs in tight sandstone exhibits reservoir selectivity, meaning that superior sandstone reservoirs with better physical properties preferentially accumulate gas, while tight sandstone layers with poorer physical properties have a weaker ability to capture gas. Therefore, gas well development indicators vary significantly across different well areas. In the He8 section of the Mizhi gas field in the Changqing Oilfield, the effective reservoir controlling factors are unclear. Drilling data reveals that even in the main channel sand bodies with favorable tectonics and sedimentary locations, although the sand bodies are thick, most are gas-free, with only a few reservoirs with good physical properties being rich in gas. While sedimentary microfacies studies can help select sand bodies, they suffer from limitations such as large vertical scale, limited core data, and a lack of constraint parameters, often relying on typical curve morphology, sandstone thickness, and sand-to-soil ratio. These limitations prevent accurate description of dominant gas-bearing reservoirs, resulting in insufficient precision in drilling location selection. Summary of the Invention

[0004] The purpose of this invention is to provide a refined geological modeling and development potential analysis method, which solves the problem that existing technologies cannot accurately describe advantageous gas-bearing reservoirs, resulting in insufficient precision in drilling location selection.

[0005] The technical solution adopted in this invention is a method for refined geological modeling and development potential analysis, comprising the following steps: S1. The gas dominant enrichment region is evaluated using a combination of fuzzy mathematics and hierarchical analysis to obtain the evaluation value; S2. Geological modeling and numerical simulation studies were optimized. Based on the evaluation values, the remaining gas reserves were predicted, and the prediction results were obtained. S3. Based on the prediction results, conduct research on stratified residual gas potential tapping and well network optimization with an economic benefit orientation.

[0006] The invention is further characterized by: The specific process of S1 is as follows: S1.1 Determine the set of factors influencing the evaluation indicators; Specifically, this involves identifying key factors affecting the development potential of gas reservoirs, including reservoir porosity, permeability, thickness, connectivity, heterogeneity, and formation pressure; and collecting well logging data, drilling data, and geological statistics. S1.2 Construct the evaluation set; Specifically, this involves: determining the number of evaluation levels (n) based on the requirements and defining the specific meaning of each evaluation level; S1.3 Establish a single-factor evaluation matrix; Specifically, a single-factor evaluation matrix is ​​established for each influencing factor, with rows representing different influencing factors and columns representing different evaluation levels; the values ​​in the matrix are then filled in based on the actual data. S1.4 Determine the weights of indicators at each level; Specifically, this involves selecting a weighting method, applying the selected method, and determining the weight of each influencing factor. S1.5 Quantify the evaluation set; Specifically, the qualitative evaluation level is converted into a quantitative value, and the comprehensive evaluation value of each influencing factor is calculated according to formula (1) using the weight and quantitative value. (1); in, S i It is the first i The comprehensive evaluation value of each influencing factor; w ij It is the first i The influencing factor in the first j The weight of each evaluation level v j It is the first jThe quantitative value of each evaluation level.

[0007] The weight determination method should be one of the following: grey relational analysis, expert scoring, or empirical analysis.

[0008] The specific process of S2 is as follows: S2.1 Geological Modeling; S2.1.1 Collect geological data including seismic data, drilling data, well logging data, and production dynamic data, and preprocess the data; Preprocessing includes cleaning, removing outliers and incomplete records; S2.1.2 Using geostatistical methods, spatial interpolation is performed on key parameters of reservoir porosity and permeability; the search radius, interpolation radius, and interpolation parameters of the variogram are determined, and model fitting is performed; S2.1.3. Using geological modeling software, import the preprocessed geological data, construct a three-dimensional geological model, simulate the connectivity and heterogeneity of the reservoir, and verify the model. S2.2 Numerical simulation; S2.2.1 Based on the three-dimensional geological model, set the parameters of the numerical simulation model, including porosity, permeability, reservoir pressure, and fluid properties; determine the simulation time step, spatial grid, and boundary conditions; S2.2.2 Initialize the geological model, fluid properties, and boundary conditions model parameters; use numerical simulation software to simulate fluid flow in the reservoir and dynamic changes in the gas reservoir; output the simulation results of pressure, flow rate, and reserves; S2.2.3 Analyze the simulation results to predict the scale and distribution pattern of the remaining gas reserves; use the simulation results to optimize the geological model and development plan.

[0009] For geological modeling, Petrel or GeoFrame modeling software should be used.

[0010] The numerical simulation software to be used is either Eclipse or CMG.

[0011] The specific process of S3 is as follows: S3.1. Determine the reasonable well spacing and the distribution characteristics of remaining reserves; S3.1.1 Analyze the connectivity and heterogeneity of the reservoir using a three-dimensional geological model and production data to determine a reasonable well spacing; estimate the well spacing according to formula (2); (2); in, d It is the well spacing; A It refers to the area controlled by a single well; S3.1.2. Using three-dimensional geological models and numerical simulation results, identify the distribution characteristics of remaining reserves; analyze the utilization of reserves in different strata and regions, and determine the priority of potential tapping; S3.2 Research on strategies for tapping the potential of stratified residual gas; S3.2.1. Under the premise of ensuring economic benefits, conduct research on stratified residual gas potential tapping strategies; calculate the economic benefits of different potential tapping schemes, including costs, benefits and net present value; S3.2.2 Based on the economic benefit analysis results, design a tiered potential tapping scheme; consider the reserve utilization rate, development cost and economic benefits of different layers, and optimize the potential tapping strategy; S3.3 Well pattern adjustment and well type optimization; S3.3.1. Based on the distribution characteristics of remaining reserves and economic benefit analysis, adjust the well network; considering the connectivity and heterogeneity of reservoirs, optimize the well network layout and improve the reserve utilization rate. S3.3.2 Select well types suitable for reservoir characteristics to improve development efficiency; S3.3.3 Develop a drilling plan that includes drilling sequence, drilling depth, and drilling angle; adjust drilling parameters; adjust drilling pressure according to formation hardness and drill bit type; adjust rotation speed according to drill bit type and formation hardness; optimize drilling path using numerical simulation results.

[0012] The beneficial effects of this invention are: The refined geological modeling and development potential analysis method provided by this invention forms a geological mechanism research technology for the development effect of multi-layered tight sandstone reservoirs; a refined characterization technology for three-dimensional geological models and numerical models; proposes efficient development technology strategies for multi-layered gas reservoirs, realizes the development potential of blocks, and formulates development adjustment strategies and well location deployment plans; and can accurately describe advantageous gas-bearing reservoirs. Detailed Implementation

[0013] The present invention will now be described in detail with reference to specific embodiments.

[0014] Example 1 The refined geological modeling and development potential analysis method proposed in this embodiment includes the following steps: S1. The gas dominant enrichment region is evaluated using a combination of fuzzy mathematics and hierarchical analysis to obtain the evaluation value; S2. Geological modeling and numerical simulation studies were optimized. Based on the evaluation values, the remaining gas reserves were predicted, and the prediction results were obtained. S3. Based on the prediction results, conduct research on stratified residual gas potential tapping and well network optimization with an economic benefit orientation.

[0015] Example 2 The refined geological modeling and development potential analysis method proposed in this embodiment includes the following steps: S1. The gas dominant enrichment region is evaluated using a combination of fuzzy mathematics and hierarchical analysis to obtain the evaluation value; The specific process is as follows: S1.1 Determine the set of factors influencing the evaluation indicators; Specifically, this involves identifying key factors affecting the development potential of gas reservoirs, including reservoir porosity, permeability, thickness, connectivity, heterogeneity, and formation pressure; and collecting well logging data, drilling data, and geological statistics. S1.2 Construct the evaluation set; Specifically, this involves: determining the number of evaluation levels (n) based on the requirements and defining the specific meaning of each evaluation level; S1.3 Establish a single-factor evaluation matrix; Specifically, a single-factor evaluation matrix is ​​established for each influencing factor, with rows representing different influencing factors and columns representing different evaluation levels; the values ​​in the matrix are then filled in based on the actual data. S1.4 Determine the weights of indicators at each level; Specifically, this involves selecting a weighting method, applying the selected method, and determining the weight of each influencing factor. S1.5 Quantify the evaluation set; Specifically, the qualitative evaluation level is converted into a quantitative value, and the comprehensive evaluation value of each influencing factor is calculated according to formula (1) using the weight and quantitative value. (1); in, S i It is the first i The comprehensive evaluation value of each influencing factor; w ij It is the first i The influencing factor in the first j The weight of each evaluation level v j It is the first j The quantitative value of each evaluation level; S2. Geological modeling and numerical simulation studies were optimized. Based on the evaluation values, the remaining gas reserves were predicted, and the prediction results were obtained. S3. Based on the prediction results, conduct research on stratified residual gas potential tapping and well network optimization with an economic benefit orientation.

[0016] Example 3 The refined geological modeling and development potential analysis method proposed in this embodiment includes the following steps: S1. The gas dominant enrichment region is evaluated using a combination of fuzzy mathematics and hierarchical analysis to obtain the evaluation value; The specific process is as follows: S1.1 Determine the set of factors influencing the evaluation indicators; Specifically, this involves identifying key factors affecting the development potential of gas reservoirs, including reservoir porosity, permeability, thickness, connectivity, heterogeneity, and formation pressure; and collecting well logging data, drilling data, and geological statistics. S1.2 Construct the evaluation set; Specifically, this involves: determining the number of evaluation levels (n) based on the requirements and defining the specific meaning of each evaluation level; S1.3 Establish a single-factor evaluation matrix; Specifically, a single-factor evaluation matrix is ​​established for each influencing factor, with rows representing different influencing factors and columns representing different evaluation levels; the values ​​in the matrix are then filled in based on the actual data. S1.4 Determine the weights of indicators at each level; Specifically, this involves selecting a weighting method, applying the selected method, and determining the weight of each influencing factor. The weight determination method should be one of the following: grey relational analysis, expert scoring, or empirical analysis. S1.5 Quantify the evaluation set; Specifically, the qualitative evaluation level is converted into a quantitative value, and the comprehensive evaluation value of each influencing factor is calculated according to formula (1) using the weight and quantitative value. (1); in, S i It is the first i The comprehensive evaluation value of each influencing factor; w ij It is the first i The influencing factor in the first j The weight of each evaluation level v j It is the first j The quantitative value of each evaluation level; S2. Geological modeling and numerical simulation studies were optimized. Based on the evaluation values, the remaining gas reserves were predicted, and the prediction results were obtained. The specific process is as follows: S2.1 Geological Modeling; S2.1.1 Collect geological data including seismic data, drilling data, well logging data, and production dynamic data, and preprocess the data; Preprocessing includes cleaning, removing outliers and incomplete records; S2.1.2 Using geostatistical methods, spatial interpolation is performed on key parameters of reservoir porosity and permeability; the search radius, interpolation radius, and interpolation parameters of the variogram are determined, and model fitting is performed; S2.1.3. Using geological modeling software, import the preprocessed geological data, construct a three-dimensional geological model, simulate the connectivity and heterogeneity of the reservoir, and verify the model. The geological modeling software to be used is either Petrel or GeoFrame. S2.2 Numerical simulation; S2.2.1 Based on the three-dimensional geological model, set the parameters of the numerical simulation model, including porosity, permeability, reservoir pressure, and fluid properties; determine the simulation time step, spatial grid, and boundary conditions; S2.2.2 Initialize the geological model, fluid properties, and boundary conditions model parameters; use numerical simulation software to simulate fluid flow in the reservoir and dynamic changes in the gas reservoir; output the simulation results of pressure, flow rate, and reserves; The numerical simulation software to be used is either Eclipse or CMG. S2.2.3 Analyze the simulation results to predict the scale and distribution pattern of the remaining gas reserves; use the simulation results to optimize the geological model and development plan; S3. Based on the prediction results, conduct research on stratified residual gas potential tapping and well network optimization with an economic benefit orientation.

[0017] Example 4 The refined geological modeling and development potential analysis method proposed in this embodiment includes the following steps: S1. The gas dominant enrichment region is evaluated using a combination of fuzzy mathematics and hierarchical analysis to obtain the evaluation value; The specific process is as follows: S1.1 Determine the set of factors influencing the evaluation indicators; Specifically, this involves identifying key factors affecting the development potential of gas reservoirs, including reservoir porosity, permeability, thickness, connectivity, heterogeneity, and formation pressure; and collecting well logging data, drilling data, and geological statistics. S1.2 Construct the evaluation set; Specifically, this involves: determining the number of evaluation levels (n) based on the requirements and defining the specific meaning of each evaluation level; S1.3 Establish a single-factor evaluation matrix; Specifically, a single-factor evaluation matrix is ​​established for each influencing factor, with rows representing different influencing factors and columns representing different evaluation levels; the values ​​in the matrix are then filled in based on the actual data. S1.4 Determine the weights of indicators at each level; Specifically, this involves selecting a weighting method, applying the selected method, and determining the weight of each influencing factor. The weight determination method should be one of the following: grey relational analysis, expert scoring, or empirical analysis. S1.5 Quantify the evaluation set; Specifically, the qualitative evaluation level is converted into a quantitative value, and the comprehensive evaluation value of each influencing factor is calculated according to formula (1) using the weight and quantitative value. (1); in, S i It is the first i The comprehensive evaluation value of each influencing factor; w ij It is the first i The influencing factor in the first j The weight of each evaluation level v j It is the first j The quantitative value of each evaluation level; S2. Geological modeling and numerical simulation studies were optimized. Based on the evaluation values, the remaining gas reserves were predicted, and the prediction results were obtained. The specific process is as follows: S2.1 Geological Modeling; S2.1.1 Collect geological data including seismic data, drilling data, well logging data, and production dynamic data, and preprocess the data; Preprocessing includes cleaning, removing outliers and incomplete records; S2.1.2 Using geostatistical methods, spatial interpolation is performed on key parameters of reservoir porosity and permeability; the search radius, interpolation radius, and interpolation parameters of the variogram are determined, and model fitting is performed; S2.1.3. Using geological modeling software, import the preprocessed geological data, construct a three-dimensional geological model, simulate the connectivity and heterogeneity of the reservoir, and verify the model. The geological modeling software to be used is either Petrel or GeoFrame. S2.2 Numerical simulation; S2.2.1 Based on the three-dimensional geological model, set the parameters of the numerical simulation model, including porosity, permeability, reservoir pressure, and fluid properties; determine the simulation time step, spatial grid, and boundary conditions; S2.2.2 Initialize the geological model, fluid properties, and boundary conditions model parameters; use numerical simulation software to simulate fluid flow in the reservoir and dynamic changes in the gas reservoir; output the simulation results of pressure, flow rate, and reserves; The numerical simulation software to be used is either Eclipse or CMG. S2.2.3 Analyze the simulation results to predict the scale and distribution pattern of the remaining gas reserves; use the simulation results to optimize the geological model and development plan; S3. Based on the prediction results, conduct research on economic benefit-oriented stratified residual gas tapping and well network optimization; The specific process is as follows: S3.1. Determine the reasonable well spacing and the distribution characteristics of remaining reserves; S3.1.1 Analyze the connectivity and heterogeneity of the reservoir using a three-dimensional geological model and production data to determine a reasonable well spacing; estimate the well spacing according to formula (2); (2); in, d It is the well spacing; A It refers to the area controlled by a single well; S3.1.2. Using three-dimensional geological models and numerical simulation results, identify the distribution characteristics of remaining reserves; analyze the utilization of reserves in different strata and regions, and determine the priority of potential tapping; S3.2 Research on strategies for tapping the potential of stratified residual gas; S3.2.1. Under the premise of ensuring economic benefits, conduct research on stratified residual gas potential tapping strategies; calculate the economic benefits of different potential tapping schemes, including costs, benefits and net present value; S3.2.2 Based on the economic benefit analysis results, design a tiered potential tapping scheme; consider the reserve utilization rate, development cost and economic benefits of different layers, and optimize the potential tapping strategy; S3.3 Well pattern adjustment and well type optimization; S3.3.1. Based on the distribution characteristics of remaining reserves and economic benefit analysis, adjust the well network; considering the connectivity and heterogeneity of reservoirs, optimize the well network layout and improve the reserve utilization rate. S3.3.2 Select well types suitable for reservoir characteristics to improve development efficiency; S3.3.3 Develop a drilling plan that includes drilling sequence, drilling depth, and drilling angle; adjust drilling parameters; adjust drilling pressure according to formation hardness and drill bit type; adjust rotation speed according to drill bit type and formation hardness; optimize drilling path using numerical simulation results.

Claims

1. A method for refined geological modeling and development potential analysis, characterized in that, Includes the following steps: S1. The gas dominant enrichment region is evaluated using a combination of fuzzy mathematics and hierarchical analysis to obtain the evaluation value; S2. Geological modeling and numerical simulation studies were optimized. Based on the evaluation values, the remaining gas reserves were predicted, and the prediction results were obtained. S3. Based on the prediction results, conduct research on stratified residual gas potential tapping and well network optimization with an economic benefit orientation.

2. The method for refined geological modeling and development potential analysis according to claim 1, characterized in that, The specific process of S1 is as follows: S1.1 Determine the set of factors influencing the evaluation indicators; Specifically, this involves identifying key factors affecting the development potential of gas reservoirs, including reservoir porosity, permeability, thickness, connectivity, heterogeneity, and formation pressure; and collecting well logging data, drilling data, and geological statistics. S1.2 Construct the evaluation set; Specifically: Based on the requirements, determine the number of evaluation levels n, where n is set to 5, representing five different evaluation levels; and define the specific meaning of each evaluation level as "Excellent", "Good", "Average", "Poor", and "Very Poor"; S1.3 Establish a single-factor evaluation matrix; Specifically, a single-factor evaluation matrix is ​​established for each influencing factor, where the rows of the single-factor evaluation matrix represent different influencing factors and the columns represent different evaluation levels; the values ​​in the matrix are then filled in based on actual data. S1.4 Determine the weights of indicators at each level; Specifically, this involves selecting a weighting method, applying the selected method, and determining the weight of each influencing factor. S1.5 Quantify the evaluation set; Specifically, the qualitative evaluation level is converted into a quantitative value, and "excellent", "good", "medium", "poor" and "extremely poor" are quantified as 5, 4, 3, 2 and 1 respectively; using the weight and quantified value, the comprehensive evaluation value of each influencing factor is calculated according to formula (1); （1）； in, S i It is the first i The comprehensive evaluation value of each influencing factor; w ij It is the first i The influencing factor in the first j The weight of each evaluation level v j It is the first j The quantitative value of each evaluation level.

3. The method for refined geological modeling and development potential analysis according to claim 2, characterized in that, The weight determination method shall be one of the following: grey relational analysis, expert scoring, or empirical analysis.

4. The method for refined geological modeling and development potential analysis according to claim 3, characterized in that, The specific process of S2 is as follows: S2.1 Geological Modeling; S2.1.1 Collect geological data including seismic data, drilling data, well logging data, and production dynamic data, and preprocess the data; The preprocessing includes cleaning, removing outliers and incomplete records; S2.1.2 Using geostatistical methods, spatial interpolation is performed on key parameters of reservoir porosity and permeability; the search radius, interpolation radius, and interpolation parameters of the variogram are determined, and model fitting is performed; S2.1.

3. Using geological modeling software, import the preprocessed geological data, construct a three-dimensional geological model, simulate the connectivity and heterogeneity of the reservoir, and verify the model. S2.2 Numerical simulation; S2.2.1 Based on the three-dimensional geological model, set the parameters of the numerical simulation model, including porosity, permeability, reservoir pressure, and fluid properties; determine the simulation time step, spatial grid, and boundary conditions; S2.2.2 Initialize the geological model, fluid properties, and boundary conditions model parameters; use numerical simulation software to simulate fluid flow in the reservoir and dynamic changes in the gas reservoir; output the simulation results of pressure, flow rate, and reserves; S2.2.3 Analyze the simulation results to predict the scale and distribution pattern of the remaining gas reserves; use the simulation results to optimize the geological model and development plan.

5. The method for refined geological modeling and development potential analysis according to claim 4, characterized in that, The geological modeling software used is either Petrel or GeoFrame.

6. The method for refined geological modeling and development potential analysis according to claim 5, characterized in that, The numerical simulation software used is either Eclipse simulation software or CMG simulation software.

7. The method for refined geological modeling and development potential analysis according to claim 6, characterized in that, The specific process of S3 is as follows: S3.

1. Determine the reasonable well spacing and the distribution characteristics of remaining reserves; S3.1.1 Analyze the connectivity and heterogeneity of the reservoir using a three-dimensional geological model and production data to determine a reasonable well spacing; estimate the well spacing according to formula (2); （2）； in, d It is the well spacing; A It refers to the area controlled by a single well; S3.1.

2. Using three-dimensional geological models and numerical simulation results, identify the distribution characteristics of remaining reserves; analyze the utilization of reserves in different strata and regions, and determine the priority of potential tapping; S3.2 Research on strategies for tapping the potential of stratified residual gas; S3.2.

1. Under the premise of ensuring economic benefits, conduct research on stratified residual gas potential tapping strategies; calculate the economic benefits of different potential tapping schemes, including costs, benefits and net present value; S3.2.2 Based on the economic benefit analysis results, design a tiered potential tapping scheme; consider the reserve utilization rate, development cost and economic benefits of different layers, and optimize the potential tapping strategy; S3.3 Well pattern adjustment and well type optimization; S3.3.

1. Based on the distribution characteristics of remaining reserves and economic benefit analysis, adjust the well network; considering the connectivity and heterogeneity of reservoirs, optimize the well network layout and improve the reserve utilization rate. S3.3.2 Select well types suitable for reservoir characteristics to improve development efficiency; S3.3.3 Develop a drilling plan that includes drilling sequence, drilling depth, and drilling angle; adjust drilling parameters; adjust drilling pressure according to formation hardness and drill bit type; adjust rotation speed according to drill bit type and formation hardness; optimize drilling path using numerical simulation results.

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