Method for establishing reservoir parameter model

Abstract

The invention discloses a method for establishing a reservoir parameter model, belongs to the technical field of geologic models, and discloses the method for establishing the reservoir parameter model, which comprises the following steps: S1, preparing modeling data; s2, determining a modeling range, a modeling unit and the size of a modeling grid; s3, building a construction model; s4, establishing a lithofacies and sedimentary microfacies model; s5, carrying out attribute modeling; the invention provides a method capable of establishing a model with a better matching relationship among reservoir physical property parameters, so that the method has certain help for reservoir quantitative research and multidisciplinary comprehensive integration, three-dimensional quantification and visual prediction of inter-well reservoirs, can effectively help reservoir physical property research, and is suitable for establishing a geologic model.

Description

Technical Field

[0001] The invention relates to the technical field of establishing geological models, and in particular to a method for establishing a reservoir parameter model. Background Art

[0002] A geological model refers to a data body that can quantitatively represent underground geological characteristics and the three-dimensional spatial distribution of various oil reservoirs. There are two main modeling methods: deterministic modeling and stochastic modeling. It comprehensively utilizes various geological, seismic, logging and dynamic data, and uses geostatistics and modeling methods to predict the spatial distribution of reservoir parameters. Reservoir geological modeling for oil field development requires not only faithfulness to the measured data of the control points, but also interpolation and extrapolation of well data with a certain degree of accuracy. The reservoir geological model is the basis for dynamic research and reservoir numerical simulation in oil field development.

[0003] Therefore, it is necessary to establish a three-dimensional model that is quantitative, capable of visual prediction, and has a better matching relationship between reservoir physical property parameters. Summary of the invention

[0004] The present invention aims to provide a method for establishing a reservoir parameter model, so as to establish a model with a better matching relationship between reservoir physical property parameters.

[0005] In order to achieve the above object, the present invention provides the following technical solution: a method for establishing a reservoir parameter model, comprising the following steps: S1. Modeling data preparation, including: reservoir distribution characteristics in the study area, well location coordinates and well trajectory data of all wells in the study area, logging curves and logging interpretation data of each single well, sub-layer stratification data of all wells, lithofacies distribution map of each sub-layer and gas-bearing area distribution map; S2. Determine the modeling scope, modeling unit and modeling grid size, specifically: divide the modeling scope according to research needs, divide the modeling unit according to the division of gas layer groups and small layers, and determine the grid size according to the interlayer characteristics reflected by the logging interpretation results; S3. Establishment of structural model; specifically, it is divided into two parts: stratigraphic framework modeling and intra-layer subdivision. The stratigraphic framework modeling uses single-well layered data to correct and control the top surface of the small layer, and the structural top surface is formed by interpolation of layered data; In-layer subdivision, according to the reservoir distribution characteristics, appropriate stratigraphic superposition types are selected to perform layer grid subdivision and establish the final structural model; S4, establishment of lithofacies and sedimentary microfacies models; i.e. reservoir facies modeling, which is established on the basis of the structural model in step S3. If the modeling is based on sandstone and mudstone as the main lithology, the modeling is based on the single well logging interpretation results, and the variogram analysis is performed on each group in step S2. Based on this, the sandstone facies model is established using the random simulation method of sequential indication. The obtained model is evaluated to obtain the optimal model, and then based on the optimal sandstone facies model, the well information is used as the hard constraint data, and the human-computer interaction method is used to make corrections, and finally a three-dimensional sandstone facies model that conforms to geological knowledge is obtained; If the modeling is mainly for carbonate reservoirs, an effective reservoir lithology model is directly established based on the well logging interpretation results and conventional geological knowledge; S5. Attribute modeling, including porosity model, permeability model, gas saturation model and net-to-gross ratio model. Based on the logging interpretation data of single wells in the study area and the lithology model, the reservoir physical property parameters of the study area are simulated and determined according to the modeling method in step S4, and the porosity, permeability, gas saturation and net-to-gross ratio models of gas reservoirs are established.

[0006] Preferably, in step S1, the logging data of each single well needs to be preprocessed, including logging curve environmental correction and logging curve standardization, and quality detection of data from different sources is required.

[0007] Preferably, the stratigraphic superposition types in step S3 include three types: proportional type, dissection type and overlay type.

[0008] Preferably, the models of the variogram in step S4 mainly include three types: exponential model, spherical model and Gaussian model.

[0009] The modeling of the present invention can make the established reservoir physical property parameter models have a better matching relationship. Therefore, when establishing the storage parameter model, the present invention takes the relevant constraint relationship of the spatial distribution of the reservoir physical property parameters as the simulation order principle, adopts the phase-controlled random modeling method, and completes the establishment of the reservoir physical property parameter distribution model one by one from the reservoir porosity, permeability, gas saturation to net-to-gross ratio, which can more clearly visualize and observe the relationship between the reservoir physical property parameters. It can facilitate the subsequent research and provide a certain theoretical basis. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is a thickness map of the superimposed sandstone of the Shan 2 section in the Zhongchangdong area according to an embodiment of the present invention.

[0011] Figure 2 This is the modeling scope of the gas reservoir geological model of the Yan 451 well area in the embodiment of the present invention.

[0012] Figure 3This is the stratigraphic framework model of the Yan 451 well area in an embodiment of the present invention.

[0013] Figure 4 This is a top surface structural diagram of the box 8 of the extension 451 well area according to an embodiment of the present invention.

[0014] Figure 5 This is a structural diagram of the top surface of Mountain 21 in the Yan 451 well area according to an embodiment of the present invention.

[0015] Figure 6 This is a structural diagram of the top surface of Mountain 23 in the Yan 451 well area according to an embodiment of the present invention.

[0016] Figure 7 This is a diagram of the main range direction variation function of the Shan 23 sandstone phase in the Yan 451 well area according to an embodiment of the present invention.

[0017] Figure 8 This is a three-dimensional map of the reservoir lithology in the Yan 451 well area according to an embodiment of the present invention.

[0018] Fig. 9 This is a grid diagram of the three-dimensional reservoir lithology model of the Yan 451 well area in an embodiment of the present invention.

[0019] Fig.10 This is an east-west section of the reservoir lithology model of the Yan 451 well area in an embodiment of the present invention.

[0020] Fig.11 This is a north-south section of the reservoir lithology model of the Yan 451 well area in an embodiment of the present invention.

[0021] Fig.12 This is a three-dimensional reservoir porosity model of the Yan 451 well area in an embodiment of the present invention.

[0022] Fig.13 This is a grid diagram of the reservoir porosity model of the Yan 451 well area in an embodiment of the present invention.

[0023] Fig.14 This is a three-dimensional reservoir permeability model of the Yan 451 well area in an embodiment of the present invention.

[0024] Fig.15 This is a grid diagram of the reservoir permeability model of the Yan 451 well area in an embodiment of the present invention.

[0025] Fig.16 This is a three-dimensional model of gas saturation of the reservoir in the Yan 451 well area according to an embodiment of the present invention.

[0026] Fig.17 This is a grid diagram of the gas saturation model of the reservoir in the Yan 451 well area according to an embodiment of the present invention.

[0027] Fig.18 This is a three-dimensional model of the net-to-gross ratio of the reservoir in the Yan 451 well area according to an embodiment of the present invention.

[0028] Fig.19This is a fence diagram of the net-to-gross ratio model of the reservoir in the Yan 451 well area according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The present invention provides a method for establishing a reservoir parameter model, comprising the following steps: S1. Modeling data preparation, including: reservoir distribution characteristics in the study area, well location coordinates and well trajectory data of all wells in the study area, logging curves and logging interpretation data of each single well, sub-layer stratification data of all wells, lithofacies distribution map of each sub-layer and gas-bearing area distribution map; S2. Determine the modeling scope, modeling unit and modeling grid size, specifically: divide the modeling scope according to research needs, divide the modeling unit according to the division of gas layer groups and small layers, and determine the grid size according to the interlayer characteristics reflected by the logging interpretation results; S3. Establishment of structural model; specifically, it is divided into two parts: stratigraphic framework modeling and intra-layer subdivision. The stratigraphic framework modeling uses single-well layered data to correct and control the top surface of the small layer, and the structural top surface is formed by interpolation of layered data; In-layer subdivision, according to the reservoir distribution characteristics, appropriate stratigraphic superposition types are selected to perform layer grid subdivision and establish the final structural model; S4, establishment of lithofacies and sedimentary microfacies models; i.e. reservoir facies modeling, which is established on the basis of the structural model in step S3. If the modeling is based on sandstone and mudstone as the main lithology, the modeling is based on the single well logging interpretation results, and the variogram analysis is performed on each group in step S2. Based on this, the sandstone facies model is established using the random simulation method of sequential indication. The obtained model is evaluated to obtain the optimal model, and then based on the optimal sandstone facies model, the well information is used as the hard constraint data, and the human-computer interaction method is used to make corrections, and finally a three-dimensional sandstone facies model that conforms to geological knowledge is obtained; If the modeling is mainly for carbonate reservoirs, an effective reservoir lithology model is directly established based on the well logging interpretation results and conventional geological knowledge; S5. Attribute modeling, including porosity model, permeability model, gas saturation model and net-to-gross ratio model. Based on the logging interpretation data of single wells in the study area and the lithology model, the reservoir physical property parameters of the study area are simulated and determined according to the modeling method in step S4, and the porosity, permeability, gas saturation and net-to-gross ratio models of gas reservoirs are established.

[0030] Preferably, in step S1, the logging data of each single well needs to be preprocessed, including logging curve environmental correction and logging curve standardization, and quality detection of data from different sources is required.

[0031] The stratigraphic superposition types described in step S3 include three types: proportional type, dissection type and overlay type.

[0032] The models of the variogram in step S4 mainly include three types: exponential model, spherical model and Gaussian model.

[0033] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments; The Zichang East area is located in the eastern part of the Yanchang exploration area. Administratively, it is located in Zichang County and Yanchuan County of Yan'an City, Shaanxi Province, and Zizhou County and Qingjian County of Yulin City. It is a low-permeability tight sandstone gas reservoir. The main gas-bearing strata are the Upper Paleozoic Benxi Formation, Shan 2, Shan 1, and He 8. The study area is Shan 2.

[0034] Reservoir distribution characteristics The thickness of the superimposed sandstone in the second section of the mountain in the eastern area of ​​Zichang is shown in the figure. Figure 1 As shown in the figure, in the southern part of the work area, the dominant phase is the underwater distributary channel sand body that is mainly distributed in the north-south direction. In the Zichang East area, there are mainly four underwater distributary channels with a width of 1.8~3.2km. There is a clear differentiation in the scale of sand body development. From the Zichang East area to the south, the main underwater distributary channel sand body belt of the high-energy environment in the Yan 145 well area and the Yan gas well area is developed, with a superimposed width of 3.8~6km and a sandstone thickness of more than 8m; four secondary underwater distributary channel sand body belts are developed, with a width of about 1.8~3km and a sandstone thickness of 4~6m. The local river channel is cut down and deepened, such as the An 17 well, with a sand thickness of 24.5m.

[0035] Basic data preparation for modeling All the wells completed in Zichang East District were logged with conventional cable logging. The logging series mainly uses eclips-5700. The logging items mainly include natural gamma, natural potential, well diameter, dual lateral, dual induction, acoustic time difference, lithology density, neutron, etc. The logging data are rich and of high quality, which can meet the requirements of this reserve calculation. A total of 20 items of various analyses and tests were completed in the target layer, including 7532 samples of conventional analysis such as porosity, permeability, and saturation, 838 samples of special analysis such as rock electrical experiments, mercury injection and phase permeability, and 856 blocks of microscopic analysis such as castings, electron microscopes and images. The core analysis and testing are in compliance with the specifications, and the data are rich and qualified, which can truly reflect the geological characteristics of the gas reservoir.

[0036] Logging data preprocessing includes logging curve environmental correction and logging curve standardization. In this reserve calculation, borehole and mud invasion corrections were performed on the natural gamma GR logging curve and the compensated neutron CNL logging curve. The trend method was used to standardize the acoustic time difference curve, and the correction amount for the reservoir section was between 0.5μs / m and 7.5μs / m, with an average of 1.1μs / m.

[0037] This modeling was carried out on the reservoir of the Yan 451 well area. Combined with the data of the gas reservoir in the Yan 451 well area, the drilling and logging data of 666 wells in the research area were collected, analyzed and sorted out. It mainly includes (1) the well location coordinates and well trajectory data of 666 wells from He 8 to Benxi in the Yan 451 well area gas reservoir; (2) the single well logging curves and interpretation conclusions of the 666 wells in the Yan 451 well area gas reservoir; (3) the sub-layer stratification data of the 666 wells in the Yan 451 well area gas reservoir; (4) the lithofacies distribution map and gas-bearing area distribution map of each sub-layer. At the same time, a comprehensive quality check will be carried out on all types of data.

[0038] Determination of modeling scope, modeling unit and modeling grid size (1) Determination of the scope of 3D geological modeling According to the needs of the Yan 451 well area gas reservoir engineering research, the modeling range of the gas reservoir 3D geological model is set as follows: Figure 2 The range is enclosed by the solid black line shown.

[0039] (2) Determination of modeling units of 3D geological model According to the division of gas reservoir groups and sublayers in the Yan 451 well area, the modeling unit division is shown in Table 1.

[0040] Table 1. Modeling unit division table of Yan 451 well area gas reservoir geological model (3) Determination of modeling grid size According to the sedimentary characteristics of the work area and the interlayer characteristics reflected by the logging interpretation data, the plane grid accuracy used in this modeling study is 100m×100m, and the vertical grid accuracy is about 1m on average. The number of nodes in the geological modeling grid is 695×848×209=123176240.

[0041] Construction model building The establishment of structural model includes the establishment of small layer framework model and the subdivision within the layer (1) Stratigraphic framework modeling The top surface of the Yan 451 well area used in the modeling is formed by interpolation of layered data. There is always a certain uncertainty in the interpolation and extrapolation level. Therefore, when establishing a three-dimensional structural grid, it is necessary to control and constrain it with well point layered data, and establish the structural grid of the study area under the constraints of well data.

[0042] The established structural framework of the Yan 451 well area includes 11 small layers from He 8 to Benxi, a total of 11 structural plane frameworks. The established stratigraphic framework model is as follows Figure 3 shown.

[0043] (2) Intralayer subdivision After the framework model is established, in order to more realistically reflect the vertical and horizontal changes of the sand bodies and interlayers inside the small layer, it is necessary to finely divide the small layer into equal-proportion longitudinal grid units. When subdividing the grid within a single sand layer, it is necessary to select the appropriate stratigraphic superposition type for layer grid subdivision according to its distribution characteristics, and establish a fine grid structural model that conforms to the structural characteristics of this area.

[0044] There are three types of stratigraphic superposition: (1) Proportional type: the internal layers are parallel to the top and bottom surfaces; (2) Erosional type: the internal layers are parallel to the bottom surface and intersect with the top surface at an acute angle; (3) Overlap type: the internal layers intersect with the bottom surface at an acute angle and are parallel to the top surface.

[0045] According to the structural characteristics of this area, there are no faults in the Yan 451 well area, and the thickness of each single sand layer is relatively uniform, so the proportional type can be selected for layer interpolation.

[0046] The established gas reservoir structure model of Yan 451 well area is as follows: Figure 4 As shown in the figure, the top surface structure diagram of each single sand layer in the structural model is as follows Figure 5 , Figure 6 The structures of the ancient strata Box 8, Shan 1, Shan 2, Taiyuan Formation, and Benxi Formation in the Yan 451 well area are consistent with the regional structural characteristics. The overall structure is a west-dipping monocline, and the morphology of each layer is similar. A series of low-amplitude nose-uplift structures with a small amplitude are developed on the background of the west-dipping large monocline.

[0047] Establishment of lithofacies and sedimentary microfacies models This modeling is aimed at the Upper Paleozoic, which is mainly sandstone and mudstone. Based on the results of single well logging lithology interpretation, the variation functions of Shihezi Formation, Shanxi Formation, Taiyuan Formation and Benxi Formation are analyzed respectively; on this basis, the random simulation method of sequential indication is applied to establish a random model of sandstone and mudstone phase, and the model is selected by evaluating the model; based on the selected sandstone phase model, the well information is used as hard constraint data, and the human-computer interaction method is used to further modify the sandstone phase model, and finally a three-dimensional sandstone phase model that conforms to the actual geological understanding of the study area is established. For the main carbonate reservoirs in the Lower Paleozoic, an effective reservoir lithology model is established directly based on the results of logging interpretation and the results of geological understanding.

[0048] (1) Variogram analysis There are three main models of variogram: exponential model, spherical model and Gaussian model. Different models are suitable for different geological conditions. The main stratum studied in this block belongs to fluvial facies sediments, so the spherical model is selected as the variogram model.

[0049] Before variogram analysis, data transformation is first performed on the data, and then variogram analysis is performed. First, data transformation is performed, which mainly includes input truncation, output truncation, logarithmic transformation and normality transformation.

[0050] Obtain the variogram based on the well point data: Based on the characteristics of each sedimentary unit, define search cones of different sizes, search steps, step tolerances, and angle tolerances, calculate the variation between spatial point pairs, and fit the variogram.

[0051] Through analysis, the variogram analysis results of the study area are shown in Table 2, where Figure 7 It is the variation function of the Shanxi Formation in the main layer.

[0052] Table 2 Results of variogram analysis of the He8-Benxi Formation in the Yan451 well area Horizon type Direction (°) Main variable range (m) Second range (m) Vertical range (m) Box 8 spherical 0 2185.2 1605.5 8.7 Shanxi Group spherical 10 2340.2 1958.6 7.5 Taiyuan Group + Benxi Group spherical 0 1188.2 846 7.3 (2) Lithofacies model According to the principles of geostatistics, geological variables can be divided into two categories: discrete and continuous. The lithofacies variables in this application belong to discrete geological variables.

[0053] The modeling of the present invention is based on the results of single well logging lithology interpretation. On the basis of analyzing the variogram of the Shihezi Formation, Shanxi Formation, Taiyuan Formation and Benxi Formation, the random simulation method of sequential indication is applied. With the well information as the hard constraint data, the sandstone phase model is further modified by the human-computer interaction method. Finally, a three-dimensional sandstone phase model that conforms to the actual geological understanding of the study area is established. The reservoir lithology prediction model of the area is as follows. The final modeling results are as follows Figure 8 , Fig. 9 shown.

[0054] from Fig.10 This is the east-west section of the reservoir lithology model. Fig.11 is the north-south section of the reservoir lithology model. Fig.10 , Fig.11 It can be seen that the sand body model is consistent with the well point sample data, which shows that the algorithm and the given parameters are reasonable and the model is reliable.

[0055] Attribute Modeling The ultimate goal of three-dimensional reservoir modeling is to establish a parameter model that can reflect the spatial distribution of underground reservoir physical properties, namely, the porosity model, permeability model, gas saturation model and net-to-gross ratio model.

[0056] (1) Porosity model The porosity interpretation results obtained from the secondary interpretation of well logging are loaded into the software, and normal transformation is performed. Then, the variogram analysis is performed on each layer in different lithofacies zones to determine the parameters required for modeling. Finally, under the control of lithofacies, the porosity model is established using the sequential Gaussian simulation method, such as Fig.12 , 13 Shown are the porosity model diagram and the porosity model fence diagram respectively.

[0057] (2) Permeability model The permeability results obtained from the secondary interpretation of well logging are loaded into the software for normal transformation. Then, the variogram analysis is performed on each layer in different lithofacies zones to determine the parameters required for modeling. Finally, under lithofacies control, the sequential Gaussian simulation method is used in conjunction with the porosity model to establish the permeability model. Fig.14 , 15 Shown are the permeability model diagram and the permeability model fence diagram respectively.

[0058] (3) Gas saturation model The gas saturation interpretation results obtained from the secondary interpretation of well logging are loaded into the software for normal transformation. Then, the variogram analysis is performed on each layer in different lithofacies zones to determine the parameters required for modeling. Finally, under lithofacies control, the sequential Gaussian simulation method is used in conjunction with the porosity model to establish the gas saturation model. Fig.16 , 17 Shown are a gas saturation model diagram and a gas saturation model fence diagram.

[0059] (4) Net-to-gross ratio model Based on the effective layer data interpreted from single well logging, the gas-bearing area of ​​each small layer in each sand layer group in the Yan 451 well area was re-delineated. Based on this gas-bearing area, the net-to-gross ratio model of the gas reservoir was determined using the established gas reservoir lithology, such as Fig.18 , 19 Shown are the net-to-gross ratio model diagram and the net-to-gross ratio model fence diagram.

[0060] Finally, under the guidance of the model established by the present invention and the subsequent research results on the reservoir research characteristics, reservoir pore structure characteristics, reservoir physical property characteristics, reservoir sensitivity characteristics, water production characteristics, etc., the gas drilling thickness of the straight directional wells in the Zichang East area has been significantly improved, with an average sandstone drilling thickness of 13.1 meters in the Shan 2 section and an average gas layer drilling thickness of 8.5 meters, which is significantly improved compared with the previous average drilling thickness of 7.0 meters; the gas encounter rate of horizontal wells in the Zichang East area has been greatly improved, with the gas layer drilling rate in the horizontal section reaching 86.2%, which is significantly improved compared with the previous average gas layer drilling rate of 82% in the horizontal section of the horizontal wells in the Zichang East area; in terms of gas testing, 31 wells were tested in the Shan 2 section, with an average open-flow rate of 48,600 cubic meters / day, compared with the previous 225 conventional gas test wells in the Shan 2 section, with an average open-flow rate of 45,300 cubic meters / day, and the gas test effect has been greatly improved.

[0061] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical solutions or characteristics in the solution is not described in detail here. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the technical solution of the present invention, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for establishing a reservoir parameter model, characterized in that: The following steps are involved: S1. Modeling data preparation, including: reservoir distribution characteristics in the study area, well location coordinates and well trajectory data of all wells in the study area, logging curves and logging interpretation data of each single well, sub-layer stratification data of all wells, lithofacies distribution map of each sub-layer and gas-bearing area distribution map; S2. Determine the modeling scope, modeling unit and modeling grid size, specifically: divide the modeling scope according to research needs, divide the modeling unit according to the division of gas layer groups and small layers, and determine the grid size according to the interlayer characteristics reflected by the logging interpretation results; S3. Establishment of structural model; specifically, it is divided into two parts: stratigraphic framework modeling and intra-layer subdivision. The stratigraphic framework modeling uses single-well layered data to correct and control the top surface of the small layer, and the structural top surface is formed by interpolation of layered data; In-layer subdivision, according to the reservoir distribution characteristics, appropriate stratigraphic superposition types are selected to perform layer grid subdivision and establish the final structural model; S4, establishment of lithofacies and sedimentary microfacies models; i.e. reservoir facies modeling, which is established on the basis of the structural model in step S3. If the modeling is based on sandstone and mudstone as the main lithology, the modeling is based on the single well logging interpretation results, and the variogram analysis is performed on each group in step S2. Based on this, the sandstone facies model is established using the random simulation method of sequential indication. The obtained model is evaluated to obtain the optimal model, and then based on the optimal sandstone facies model, the well information is used as the hard constraint data, and the human-computer interaction method is used to make corrections, and finally a three-dimensional sandstone facies model that conforms to geological knowledge is obtained; If the modeling is mainly for carbonate reservoirs, an effective reservoir lithology model is directly established based on the well logging interpretation results and conventional geological knowledge; S5. Attribute modeling, including porosity model, permeability model, gas saturation model and net-to-gross ratio model. Based on the logging interpretation data of single wells in the study area and the lithology model, the reservoir physical property parameters of the study area are simulated and determined according to the modeling method in step S4, and the porosity, permeability, gas saturation and net-to-gross ratio models of gas reservoirs are established.

2. A method for establishing a reservoir parameter model according to claim 1, characterized in that: In step S1, the logging data of each single well needs to be preprocessed, including logging curve environmental correction and logging curve standardization, and quality inspection of data from different sources is required.

3. A method for establishing a reservoir parameter model according to claim 1, characterized in that: The stratigraphic superposition types in step S3 include three types: proportional type, dissection type and overlay type.

4. The method for establishing a reservoir parameter model according to claim 1, characterized in that: The models of the variogram in step S4 mainly include exponential model, spherical model and Gaussian model.

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