Thin reservoir modeling method capable of accurately meeting well point and geological map constraints

Abstract

The invention discloses a thin reservoir modeling method capable of accurately meeting well point and geological map constraints, relates to a geological modeling method, and particularly relates to a thin reservoir modeling method capable of accurately meeting the well point and geological map constraints, which can keep various attribute thicknesses calculated by a model consistent with constrained original maps. Comprising the following steps that basic information of a block is loaded into modeling software, and the basic information comprises a seismic interpretation result, a coordinate well position, well deviation, well logging curve data and well point layering data; according to the method, a logging curve and test analysis data are comprehensively utilized, firstly, making of a four-relation chart and establishment of an interpretation model are completed, then logging data are processed and interpreted, and finally all parameters are subjected to layered statistics; according to the method, the quality of the model can be controlled by adopting a phase control and trend body modeling method, so that the model at the well point is ensured to be consistent with actually measured data, and the model can be matched with a constraint map in a predicted calculation area.

Description

TECHNICAL FIELD

[0001] The present application relates to a geological modeling method, in particular to a thin reservoir modeling method which can make the model calculation of various attributes thickness consistent with the original map constraints. BACKGROUND

[0002] At present, the reservoir sand body modeling method is affected by many reasons such as the incompleteness of known data, the insufficient accuracy of known data, the insufficient understanding of the reservoir, and the incomplete modeling algorithm, and the interwell prediction result of the reservoir often contains many uncertainties, which brings certain risks to the well location, well track, measures and adjustment scheme design. The stochastic simulation algorithm can be used to represent the uncertainty of the interwell prediction of the reservoir, and has important reference value for optimizing various scheme designs and reducing risks, but it is far away from the purpose of the reservoir modeling, that is, obtaining a three-dimensional geological model which most closely approximates the actual underground certainty, and it is difficult for people to select the optimal modeling result from the multiple equal-probability geological models given by the stochastic modeling. In order to reduce the uncertainty of the stochastic modeling, some constraint conditions are added to the stochastic simulation algorithm, such as the planar distribution range of the sedimentary microfacies, the value range of various attribute parameters in the reservoir, the geometric characteristics of various attribute bodies in the reservoir (including the inclination and dip angle of the interlayer), or the value range of the seismic attribute (such as wave impedance), etc. The joint action of these constraint conditions greatly reduces the uncertainty of the interwell reservoir prediction.

[0003] In the process of geological modeling, a sandstone model is generally established first, and at this time, the sandstone thickness map can be used for constrained simulation. In theory, the modeling lithofacies model is a sandstone model, and the thickness extracted using the model should be consistent with the sandstone thickness drawn by the geologists. However, in actual work, due to the uncertainty caused by the use of the random algorithm, there is a large gap between the sandstone thickness map and the geological modeling model. SUMMARY

[0004] In order to solve the above problems, the present application provides a thin reservoir modeling method which can ensure that the model at the well point is consistent with the measured data, and can also make the calculation area of the prediction consistent with the constraint map.

[0005] The thin reservoir modeling method which accurately meets the well point and geological map constraints comprises the following steps: Step 101, loading the basic data of the block into the modeling software, wherein the basic data includes seismic interpretation results, coordinate well locations, well inclinations, logging curve data, and well point layering data; Step 102: By comprehensively utilizing well logging curves and laboratory analysis data, first complete the creation of the "four-property relationship" chart and the establishment of the interpretation model, then process and interpret the well logging data, and finally perform stratified statistical analysis of various parameters. Step 103: Extract the thickness data of a single sand body from the modeling software and use it as a constraint to ensure that the minimum grid thickness is less than half of the minimum single sand body thickness and that the number of grids between two wells is greater than 6 on the plane. Based on the seismic interpretation results and the well point stratification data in step 101, a structural model is established; secondly, based on the well logging interpretation results, stratification data statistics are completed; then, based on the comprehensive geological understanding, sedimentary facies diagrams, sandstone thickness contour maps, effective thickness contour maps, and porosity, permeability, and saturation contour maps are drawn. Step 104: Based on the lithology, porosity, permeability, saturation, and net-to-gross ratio data interpreted from the well point logging curves, the data are discretized into hard data. Using the sedimentary facies map, sandstone thickness contour map, effective thickness contour map, and porosity, permeability, and saturation contour map drawn in Step 103 as trend constraints, a three-dimensional trend model of the geological attributes of each stratum is established through the data analysis module. Step 105: In the process of random modeling of each geological attribute, the random modeling of each attribute is constrained by the three-dimensional trend model generated in step 104. The three-dimensional trend model is used as the control field, and the dominant migration direction determined by geological research is used as the anisotropy constraint. A random simulation algorithm is used to finally generate a three-dimensional attribute model that conforms to geological laws. Step 106, Sandstone Facies Model Quality Control and Iterative Optimization: First, extract the cumulative thickness of the sandstone facies in the 3D attribute model and verify it against the corresponding planar geological map. If there are overall discrepancies, return to adjust the lithofacies scale parameters of the trend model, or use a calculator to fine-tune the trend volume values. Through multiple iterations, make the model results approximate the constraint conditions until the accuracy requirements are met, and obtain the final sandstone facies model. Step 107, Phase-Controlled Attribute Modeling and Quality Control: Using the sandstone facies model corrected in Step 106 as a constraint framework, phase-controlled modeling methods are employed to sequentially establish porosity, permeability, saturation, and net-to-gross ratio attribute models. Through iterative optimization, it is ensured that the spatial distribution of each attribute is controlled by the sandstone facies distribution and conforms to geological laws.

[0006] Preferably, in step 105, the dominant transport direction is the ancient water flow direction.

[0007] This invention adds trend model constraints and boundary condition control to the conventional modeling process. Constraints are established in reservoir-free areas of the geological map to remove sand bodies and effective layers generated by random algorithms. By comparing the modeling results with the actual geological model, the difference between the model's thickness map and the geological constraint thickness map is statistically determined. The trend model is then applied to adjust the thickness proportionally to make corrections, thereby further aligning the model results with the geological map.

[0008] The present invention employs a phase control plus trend volume modeling method to control model quality, ensuring that the model at the well point is consistent with the measured data and that the predicted calculation area matches the constraint map. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the process of the present invention.

[0010] Figure 2 This is a structural diagram of the work area establishment in an embodiment of the present invention.

[0011] Figure 3 This is an isopleth map of sandstone thickness in the work area of ​​an embodiment of the present invention.

[0012] Figure 4 This is a vertical distribution diagram of the river channels in the work area according to an embodiment of the present invention.

[0013] Figure 5 This is a trend diagram of the ancient water flow direction in the work area according to an embodiment of the present invention.

[0014] Figure 6 This is the lithofacies probability trend body of the work area in the embodiment of the present invention.

[0015] Figure 7 This is a sedimentary microfacies model of the work area in an embodiment of the present invention.

[0016] Figure 8 This is a block porosity model of the work area in an embodiment of the present invention.

[0017] Figure 9 This is a model of the work area extracted from the sandstone thickness in an embodiment of the present invention. Detailed Implementation

[0018] This invention provides a method for modeling thin reservoirs that accurately meets the constraints of well points and geological maps, comprising the following steps: Step 101: Load the basic data of the block into the modeling software. The basic data includes seismic interpretation results, coordinate well locations, well inclination, well logging curve data, and well point stratification data. Step 102: By comprehensively utilizing well logging curves and laboratory analysis data, first complete the creation of the "four-property relationship" chart and the establishment of the interpretation model, then process and interpret the well logging data, and finally perform stratified statistical analysis of various parameters. Step 103: Extract the thickness data of a single sand body from the modeling software and use it as a constraint to ensure that the minimum grid thickness is less than half of the minimum single sand body thickness and that the number of grids between two wells is greater than 6 on the plane. Based on the seismic interpretation results and the well point stratification data in step 101, a structural model is established; secondly, based on the well logging interpretation results, stratification data statistics are completed; then, based on the comprehensive geological understanding, sedimentary facies diagrams, sandstone thickness contour maps, effective thickness contour maps, and porosity, permeability, and saturation contour maps are drawn. Step 104: Based on the lithology, porosity, permeability, saturation, and net-to-gross ratio data interpreted from the well point logging curves, the data are discretized into hard data. Using the sedimentary facies map, sandstone thickness contour map, effective thickness contour map, and porosity, permeability, and saturation contour map obtained in Step 103 as trend constraints, a three-dimensional trend model of the geological attributes of each stratum is established through the data analysis module. Step 105: In the process of random modeling of each geological attribute, the random modeling of each attribute is constrained by the three-dimensional trend model generated in step 104. The three-dimensional trend model is used as the control field, and the dominant migration direction determined by geological research is used as the anisotropy constraint. A random simulation algorithm is used to finally generate a three-dimensional attribute model that conforms to geological laws. Step 106, Sandstone Facies Model Quality Control and Iterative Optimization: First, extract the cumulative thickness of the sandstone facies in the 3D attribute model and verify it against the corresponding planar geological map. If there are overall discrepancies, return to adjust the lithofacies scale parameters of the trend model, or use a calculator to fine-tune the trend volume values. Through multiple iterations, make the model results approximate the constraint conditions until the accuracy requirements are met, and obtain the final sandstone facies model; Step 107, Phase-Controlled Attribute Modeling and Quality Control: Using the sandstone facies model corrected in Step 106 as a constraint framework, phase-controlled modeling methods are employed to sequentially establish attribute models for porosity, permeability, saturation, and net-to-gross ratio. Through iterative optimization, it is ensured that the spatial distribution of each attribute is controlled by the sandstone facies distribution and conforms to geological laws.

[0019] In step 105, the dominant transport direction is the ancient water flow direction.

[0020] In step 103, the geological property plan map includes, but is not limited to: sedimentary facies map, sandstone thickness contour map, effective thickness contour map, porosity contour map, permeability contour map, and saturation contour map.

[0021] The present invention will be further described below with reference to the accompanying drawings. The scope of protection of the present invention is not limited to the following description: This embodiment focuses on the Upper Paleozoic A gas reservoir in the Yan'an gas field of the Ordos Basin. Located in Yanchang County, Yan'an City, Shaanxi Province, the Benxi Formation in the study area is a barrier coastal sedimentary environment, while the Shanxi Formation and He8 Member exhibit deltaic deposits controlled by fluvial activity in a shallow-water setting. The main subfacies in the area is deltafront. The A gas reservoir has maintained stable production for nearly eight years. However, some wells have begun to decline, making the situation for stable production in the well area very challenging. Under the current development model, the complex superposition and strong homogeneity of the reservoirs lead to significant differences in the degree of reserve utilization.

[0022] The work area covers approximately 1141.8 km², with 348 wells. Vertically, from bottom to top, it consists of four strata: the Carboniferous Benxi Formation, the Permian Taiyuan Formation, the Shanxi Formation, and the Lower Shihezi Formation. The main gas-bearing strata are the Shihezi Formation, the First Section of the Permian, the Second Section of the Permian, and the Benxi Formation.

[0023] There is currently no seismic data in the study area. The average well spacing in the work area is about 1 km. The existing geological maps, including sandstone thickness contour maps and effective thickness contour maps, are basically complete.

[0024] according to Figure 1 The flowchart shown is as follows: Step 101: Load the basic data of the block into the modeling software. The basic data includes seismic interpretation results, coordinate well locations, well inclination, well logging curve data, and well point stratification data. Step 102: By comprehensively utilizing well logging curves and laboratory analysis data, first complete the creation and interpretation model of the "four-property relationship" chart, then process and interpret the well logging data, and finally perform stratified statistical analysis of various parameters; based on the parameter interpretation, statistical analysis of data from each layer is performed to complete sedimentary facies diagrams, sandstone thickness contour maps, effective thickness contour maps, and porosity, permeability, and saturation contour maps. Step 103: Extract the thickness data of a single sand body from the modeling software and use it as a constraint to ensure that the minimum grid thickness is less than half of the minimum single sand body thickness and that the number of grids between two wells is greater than 6 on the plane. Based on the seismic interpretation results and the well point stratification data in step 101, a structural model was established; secondly, based on the well logging interpretation results, stratification data statistics were completed; then, based on the comprehensive geological understanding, sedimentary facies maps, sandstone thickness contour maps, effective thickness contour maps, and permeability contour maps were drawn for further research. Step 104: Based on the lithology, porosity, permeability, saturation, net-to-gross ratio and other data interpreted from the well point logging curves, the data are discretized into hard data. Using the geological attribute planar map obtained in Step 103 as a trend constraint, a three-dimensional trend model of the geological attributes of each layer is established through the data analysis module. Step 105, in the stochastic modeling process of each geological attribute, the established three-dimensional trend model is used as the control field, and the dominant migration direction determined by geological research is used as the anisotropy constraint. A stochastic simulation algorithm is used to finally generate a three-dimensional attribute model that conforms to geological laws. Step 106, Model Quality Control and Iterative Optimization: First, extract the cumulative thickness of the sandstone facies from the geological attribute model and compare it with the sandstone thickness contour map generated in Step 103. If there are overall differences, return to the trend model to adjust the lithofacies scale parameters, or use a calculator to calibrate the trend volume values. Through multiple iterations, the model results continuously approach the constraint conditions until the accuracy requirements are met.

[0025] Step 107: Based on the sandstone facies model corrected in Step 106, the phase-controlled modeling method is used to establish attribute models such as porosity, permeability, saturation, and net-to-gross ratio. That is, the distribution of attribute models is controlled by the spatial distribution of sandstone facies.

[0026] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for modeling thin reservoirs that accurately meets the constraints of well points and geological maps, characterized in that, Includes the following steps: Step 101: Load the basic data of the block into the modeling software. The basic data includes seismic interpretation results, coordinate well locations, well inclination, well logging curve data, and well point stratification data. Step 102: By comprehensively utilizing well logging curves and laboratory analysis data, first complete the creation of the "four-property relationship" chart and the establishment of the interpretation model, then process and interpret the well logging data, and finally perform stratified statistical analysis of various parameters. Step 103: Extract the thickness data of a single sand body from the modeling software and use it as a constraint to ensure that the minimum grid thickness is less than half of the minimum single sand body thickness and that the number of grids between two wells is greater than 6 on the plane. Based on the seismic interpretation results and the well point stratification data in step 101, a structural model is established; secondly, based on the well logging interpretation results, stratification data statistics are completed; then, based on the comprehensive geological understanding, sedimentary facies diagrams, sandstone thickness contour maps, effective thickness contour maps, and porosity, permeability, and saturation contour maps are drawn. Step 104: Based on the lithology, porosity, permeability, saturation, and net-to-gross ratio data interpreted from the well logging curves, the data are discretized into hard data. Using the sedimentary facies map, sandstone thickness contour map, effective thickness contour map, and permeability contour map drawn in Step 103 as trend constraints, a three-dimensional trend model of the geological attributes of each stratum is established through the data analysis module. Step 105: The random modeling of each attribute is constrained by the three-dimensional trend model generated in step 104, and the dominant migration direction determined by geological research is used as the anisotropy constraint. A random simulation algorithm is used to finally generate a three-dimensional attribute model that conforms to geological laws. Step 106, quality control and iterative optimization of sandstone facies model: First, extract the cumulative thickness of sandstone facies in the three-dimensional attribute model and verify it with the corresponding planar geological map. If there is an overall difference, return to adjust the lithofacies ratio parameter of the trend model, or use a calculator to fine-tune the trend volume value. Through multiple iterations, make the model result close to the constraint conditions until the accuracy requirements are met, and obtain the final sandstone facies model. Step 107, Phase-controlled attribute modeling and quality control: Using the sandstone facies model corrected in Step 106 as a constraint framework, the phase-controlled modeling method is used to sequentially establish porosity, permeability, saturation and net-to-gross ratio attribute models. Through iterative optimization, it is ensured that the spatial distribution of each attribute is controlled by the distribution of the sandstone facies and conforms to geological laws.

2. The thin reservoir modeling method that accurately satisfies well point and geological map constraints as described in claim 1, characterized in that, In step 105, the dominant transport direction is the ancient water flow direction.

Images