A method, device and equipment for multi-point statistical modeling of oil and gas reservoir areas

Abstract

The present invention discloses a multi-point statistical modeling method, device and equipment for an oil and gas reservoir region. The method comprises: determining the morphology of a target geological body contained in a target sedimentary phase in an oil and gas reservoir region; if the morphology of the target geological body is a heterogeneous sedimentary geological body, determining the correlation between the sedimentary microfacies and well seismic data using a sedimentary microfacies spatial variation function with a non-fixed range. In the present invention, the inventor fully considers the morphology of the target geological body contained in the target sedimentary phase in the oil and gas reservoir region, that is, the inventor fully considers the anisotropic characteristics of the sedimentary geological body. The inventor determines the range of the sedimentary microfacies spatial variation function that affects the sedimentary microfacies based on the morphology of the target geological body, and then more accurately determines the correlation with the well seismic data, thereby ensuring the rationality and reliability of the variable influence ratio algorithm, and improving the variable influence ratio algorithm from the perspective of anisotropic reservoirs.

Description

Technical Field

[0001] The invention relates to the field of petroleum geology technology, and in particular to a multi-point statistical modeling method, device and equipment for an oil and gas reservoir region. Background Art

[0002] Geological modeling is a three-dimensional quantitative random model generated by computer graphics technology based on comprehensive analysis of geological, well logging, geophysical data and various interpretation results or conceptual models. Therefore, geological modeling is an interdisciplinary subject involving geology, data / information analysis, and computational science, or a subject that integrates various disciplines. The geological model established in this way summarizes various information and interpretation results. Therefore, whether to understand the advantages and disadvantages of various input data / information is the key to the reasonable integration of these data.

[0003] Multi-point statistical modeling (MPS, Multiple Point Simulation) was proposed by researchers at the Stanford University Reservoir Prediction Center. Around the development of multi-point statistical modeling methods, international professionals have proposed a number of improved algorithms, such as the Snesim algorithm, the Filtersim algorithm, and the well-seismic (well logging data and seismic data) combination algorithm. In order to reduce the uncertainty of using only well logging data for oil and gas reservoir geological modeling, many scholars have proposed and improved modeling algorithms that combine seismic data and well logging data over the past 20 years, and have made great progress. In 2002, Professor Journel of Stanford University proposed a Journel influence ratio algorithm that combines well logging data and seismic data based on the multi-point statistical modeling method, which made contributions to the practical application of the well-seismic combination algorithm.

[0004] In Professor Jurnel's paper, given two probabilistic events B and C from different sources, the conditional probability P(A|B,C) is used to solve the estimation problem of the unknown parameter A. The data A, B, and C can take values ​​at multiple spatial locations, and B and C represent well logging and seismic data, respectively. A is an unknown parameter that needs to be obtained with well logging data and seismic data as constraints. Specifically, A is the spatial distribution of sedimentary microfacies in the form of discrete variables. It cannot be directly observed, but is only a reservoir property simulated in reservoir modeling.

[0005] Assume that both conditional probabilities P(A|B) and P(A|C) can be estimated. The challenge here is that B and C are data from different sources, as well logging data and seismic data, they have a certain correlation and cannot be considered independent of each other. If the two are independent of each other, the problem can be easily solved using Bayes' theorem.

[0006] P(A|B,C) can be used to estimate or simulate event A. When there is a correlation between two data, the traditional combination algorithm under the assumption of conditional independence is unstable and various contradictions will occur. In this regard, Professor Jules Verne's most important contribution is to propose the concept of "Permanence of Updating Ratios" for probabilistic events involving many spatial locations. In the presence of complex data interdependence, this concept ensures the stability of the restrictive effects of all probability conditions. This "update ratio" is the concept of "influence ratio".

[0007] The influence ratio is a parameter that must be determined when running the soft and hard data combination algorithm. It represents the ratio of the influence of seismic data and well logging data on the simulation results. When combining well and seismic data for modeling, it provides a choice of whether the influence of seismic data is greater or the influence of well logging data is greater. In the algorithm, the influence ratio is expressed as the ratio of two integers, such as L:S. L represents the influence of well logging data, and S represents the influence of seismic data.

[0008] However, Professor Jules Verne's paper made some simplifications to the problem being solved. In this paper, he assumed the concept of constant update ratio, and could get the understanding that "before or after cognition of B, the incremental effect of data event C on unknown event A is the same." Professor Jules Verne's algorithm implements the same influence ratio for each grid node in the three-dimensional space of modeling. This simplification seriously affects the effect of well-seismic combined modeling. Because in the modeling space, the grid nodes are far from the sampling points of the logging data. For nodes that are closer to the logging data, the predicted unknown parameter A is more affected by the logging data and less affected by the seismic data. On the contrary, the logging data has a small impact and the seismic has a large impact.

[0009] Junior proposed the concept of "fixed influence ratio" (Permanence of Updating Ratios). In the modeling attribute simulation, whether each simulation node is more affected by seismic data or logging data, he used a fixed influence ratio, that is, the influence ratio implemented for each grid node in the three-dimensional space of the model is the same. This method is more suitable for relatively homogeneous oil reservoirs under dense well network conditions. Summary of the invention

[0010] The inventor of this application submitted a patent application with publication number CN103678899A on December 5, 2013, and the invention name is "A method and device for multi-point statistical modeling of oil and gas reservoirs based on variable influence ratio". The patent proposes an algorithm of variable influence ratio, which sets a circular range with the main range of sedimentary variation function as the radius to divide the influence of logging and seismic input data on simulation results. This method proposes a uniform logging data influence range, that is, the influence ratio is the same within this area. This method realizes that the influence ratio of each grid node in the space can be changed with the different positions of each simulated node relative to the well, improves the effect of well-seismic combined modeling, and provides technical support for further exploration of oil and gas reservoirs.

[0011] Although the simulation range is determined with reference to the variation function value obtained from geostatistics, a uniform circular range is formed, which does not take into account the changes in the sedimentary distribution morphology of the target sedimentary phase itself, and the simulation effect is not good for sedimentary geological bodies with strong heterogeneity.

[0012] In view of the above problems, the present invention is proposed to provide a method, device and equipment for multi-point statistical modeling of oil and gas reservoir regions that overcome the above problems or at least partially solve the above problems.

[0013] In a first aspect, an embodiment of the present invention provides a multi-point statistical modeling method for an oil and gas reservoir region, which may include:

[0014] Determine the morphology of the target geological body contained in the target sedimentary phase in the oil and gas reservoir area;

[0015] If the morphology of the target geological body is a heterogeneous sedimentary geological body, the correlation between the sedimentary microfacies and the well seismic data is determined by using the sedimentary microfacies spatial variation function with a non-fixed range.

[0016] Optionally, the heterogeneous sedimentary geological body includes at least one of the following: fluvial sand body, fluvial point sand bar body, tidal flat sand bar and marine-continental transitional delta.

[0017] Optionally, if the target geological body is a fluvial facies sand body, the length and width of the sand body distribution in the fluvial facies sand body are determined; the correlation between the sedimentary microfacies and the well seismic data is determined by the sedimentary microfacies spatial variation function of the non-fixed range composed of the length and the width;

[0018] If the target geological body is in the form of a fluvial facies point sand bar or a tidal flat facies sand bar, determine the major variation range and minor variation range of the sand body distribution of the fluvial facies point sand bar or the tidal flat facies sand bar; determine the correlation between the sedimentary microfacies and the well seismic data using the sedimentary microfacies spatial variation function of the non-fixed variation range composed of the major variation range and the minor variation range;

[0019] If the morphology of the target geological body is a marine-continental transition delta, the distribution range of the front sand body and / or coastal sand body in the marine-continental transition delta is determined, and the correlation between the sedimentary microfacies and the well seismic data is determined by the sedimentary microfacies spatial variation function of the range.

[0020] Optionally, the method may further include: collecting well logging data and seismic data corresponding to the three-dimensional current oil and gas reservoir area;

[0021] Setting a three-dimensional rectangular grid based on the spatial positions of the well logging data and the seismic data;

[0022] According to the locations where the well logging data and the seismic data are taken, the well logging data and the seismic data of the oil and gas reservoir area are assigned to corresponding grid nodes of the three-dimensional rectangular grid;

[0023] Dividing the three-dimensional rectangular grid into a first area and a second area according to the non-fixed variation range of the sedimentary microfacies spatial variation function;

[0024] determining variable influence ratios of grid nodes in the oil and gas reservoir region;

[0025] Spatial modeling is performed based on the variable influence ratios.

[0026] Optionally, after performing spatial modeling according to the variable influence ratio, the method further includes:

[0027] The oil and gas reservoir layer is determined according to the multi-point statistical spatial modeling results corresponding to the oil and gas reservoir area.

[0028] Optionally, determining the variable influence ratio of the grid nodes in the oil and gas reservoir region includes:

[0029] Determining whether the grid node falls within the first area;

[0030] If so, the variable influence ratio is dominated by the well logging data;

[0031] Otherwise, the variable influence ratio is dominated by the seismic data.

[0032] Optionally, the method may further include:

[0033] If the target geological body is a homogeneous sedimentary geological body, the correlation between the sedimentary microfacies and the well seismic data is determined by the sedimentary microfacies spatial variation function with a fixed range; accordingly,

[0034] The three-dimensional rectangular grid is divided into a first area and a second area according to the fixed variation range of the sedimentary microfacies spatial variation function.

[0035] In a second aspect, an embodiment of the present invention provides a multi-point statistical modeling device for a gas reservoir region, which may include:

[0036] A judgment module, used to judge the morphology of the target geological body contained in the target sedimentary phase;

[0037] The determination module is used to determine the correlation between the sedimentary microfacies and the well seismic data by using the sedimentary microfacies spatial variation function with a non-fixed range if the determination module determines that the morphology of the target geological body is a heterogeneous sedimentary geological body.

[0038] Optionally, the device may further include: an acquisition module for acquiring well logging data and seismic data corresponding to the three-dimensional current oil and gas reservoir area;

[0039] A setting module, used for setting a three-dimensional rectangular grid based on the spatial positions of the well logging data and the seismic data;

[0040] An assignment module, used for assigning the well logging data and seismic data of the oil and gas reservoir area to corresponding grid nodes of the three-dimensional rectangular grid according to the value locations of the well logging data and the seismic data;

[0041] A division module, used for dividing the three-dimensional rectangular grid into a first area and a second area according to the non-fixed variation range of the sedimentary microfacies spatial variation function;

[0042] The determination module is further used to determine the variable influence ratio of the grid nodes in the oil and gas reservoir area;

[0043] A modeling module is used to perform spatial modeling according to the variable influence ratio.

[0044] In a third aspect, an embodiment of the present invention provides a reservoir distribution prediction method, which may include: predicting reservoir distribution based on a geological model obtained by the multi-point statistical modeling method for the oil and gas reservoir area as described in the first aspect.

[0045] In a fourth aspect, an embodiment of the present invention provides a reservoir distribution prediction device, which may include: a prediction module and a multi-point statistical modeling device for an oil and gas reservoir region as described in the second aspect;

[0046] The prediction module is used to predict reservoir distribution based on the geological model obtained by the multi-point statistical modeling device of the oil and gas reservoir area.

[0047] In a fifth aspect, an embodiment of the present invention provides a method for analyzing oil and gas reservoir migration, which may include: analyzing oil and gas reservoir migration based on a geological model obtained by the multi-point statistical modeling method of the oil and gas reservoir area as described in the first aspect.

[0048] In a sixth aspect, an embodiment of the present invention provides an oil and gas reservoir migration analysis device, which may include: a migration analysis module and an oil and gas reservoir region multi-point statistical modeling device as described in the second aspect;

[0049] The migration analysis module is used to analyze the migration of oil and gas reservoirs based on the geological model obtained by the multi-point statistical modeling device of the oil and gas reservoir area.

[0050] In the seventh aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, it implements the multi-point statistical modeling method for the oil and gas reservoir area as described in the first aspect, or implements the reservoir distribution prediction method as described in the third aspect, or implements the oil and gas reservoir migration analysis method as described in the fifth aspect.

[0051] In an eighth aspect, an embodiment of the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for multi-point statistical modeling of an oil and gas reservoir area as described in the first aspect is implemented, or the method for predicting reservoir distribution as described in the third aspect is implemented, or the method for analyzing oil and gas reservoir migration as described in the fifth aspect is implemented.

[0052] The beneficial effects of the above technical solution provided by the embodiment of the present invention include at least:

[0053] In an embodiment of the present invention, a multi-point statistical modeling method, device and equipment for an oil and gas reservoir region are provided. The method may include: determining the morphology of a target geological body contained in a target sedimentary phase in the oil and gas reservoir region; if the morphology of the target geological body is a heterogeneous sedimentary geological body, determining the correlation between the sedimentary microfacies and the well seismic data using a sedimentary microfacies spatial variation function with a non-fixed range. In an embodiment of the present invention, the inventor fully considers the morphology of the target geological body contained in the target sedimentary phase in the oil and gas reservoir region, that is, the inventor fully considers the anisotropic characteristics of the sedimentary geological body. The inventor determines the range of the sedimentary microfacies spatial variation function that affects the well seismic data based on the morphology of the target geological body, and then more accurately determines the correlation with the well seismic data, thereby ensuring the rationality and reliability of the variable influence ratio algorithm, and improving the variable influence ratio algorithm from the perspective of anisotropic reservoirs.

[0054] Furthermore, it has special application value for horizontal wells which are widely used in oil and gas field development.

[0055] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0056] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0058] Figure 1 A flowchart of a multi-point statistical modeling method for an oil and gas reservoir region provided in an embodiment of the present invention;

[0059] Figure 2 A schematic diagram of the range-changing region of the fluvial facies sand body provided in an embodiment of the present invention;

[0060] Figure 3 A schematic diagram of a variable range area of ​​a river phase point sand bar body provided in an embodiment of the present invention;

[0061] Figure 4 A schematic diagram of the range-varying area of ​​a tidal flat sand bar provided in an embodiment of the present invention;

[0062] Figure 5 A schematic diagram of the range-changing region of a marine-continental transitional delta provided in an embodiment of the present invention;

[0063] Figure 6 A flowchart of a specific multi-point statistical modeling method for oil and gas reservoir regions provided in an embodiment of the present invention;

[0064] Figure 7 A schematic diagram of the main range coinciding with the X-axis and the secondary range coinciding with the Y-axis provided in an embodiment of the present invention;

[0065] Figure 8 A schematic diagram of an angle between the primary range and the X-axis and an angle between the secondary range and the Y-axis provided in an embodiment of the present invention;

[0066] Fig. 9 A schematic diagram of a fixed range provided in an embodiment of the present invention;

[0067] Fig.10 A schematic diagram of a fixed range division area provided in an embodiment of the present invention;

[0068] Fig.11 A schematic diagram comparing a fixed range and a non-fixed range provided in an embodiment of the present invention;

[0069] Fig.12 A schematic diagram of an earthquake profile provided in an embodiment of the present invention;

[0070] Fig.13 A schematic diagram of a geological model constructed by a variable influence ratio determined by a fixed range provided in an embodiment of the present invention;

[0071] Fig.14A schematic diagram of a geological model constructed by a variable influence ratio determined by a non-fixed range provided in an embodiment of the present invention;

[0072] Fig.15 It is a schematic diagram of the structure of the multi-point statistical modeling device for oil and gas reservoir area provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0073] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0074] Example 1

[0075] Embodiment 1 of the present invention provides a multi-point statistical modeling method for oil and gas reservoir regions, referring to Figure 1 As shown, the method may include the following steps:

[0076] Step S11, determining the morphology of the target geological body contained in the target sedimentary phase in the oil and gas reservoir area; if the morphology of the target geological body is a heterogeneous sedimentary geological body, executing step S12; otherwise, executing step S13.

[0077] Step S12: Determine the correlation between the sedimentary microfacies and the well seismic data using the sedimentary microfacies spatial variation function with a non-fixed range.

[0078] Step S13: Determine the correlation between the sedimentary microfacies and the well seismic data using the sedimentary microfacies spatial variation function with a fixed range.

[0079] In the embodiment of the present invention, the inventor fully considers the morphology of the target geological body contained in the target sedimentary phase in the oil and gas reservoir area, and the target geological body is sedimentary rock, that is, the inventor fully considers the anisotropic characteristics of the sedimentary geological body. The inventor determines the range of the spatial variation function affecting the sedimentary microfacies based on the morphology of the target geological body, and then more accurately determines the correlation with the well seismic data, thereby ensuring the rationality and reliability of the variable influence ratio algorithm, and improving the variable influence ratio algorithm from the perspective of anisotropic reservoirs. Furthermore, it has special application value for horizontal wells that are widely used in oil and gas field development.

[0080] In an optional embodiment, general sedimentary geological bodies behave isotropically during sedimentary diagenesis, but for special sedimentary geological bodies, the anisotropy they exhibit affects the mineralization, distribution, and migration of oil and gas reservoirs. The inventors fully consider the influence of the anisotropic sedimentary geological body on the determination of the variable influence ratio, and construct a geological model of the oil and gas reservoir more accurately.

[0081] The above-mentioned heterogeneous sedimentary geological body in this embodiment may include at least one of the following: fluvial phase sand body, fluvial phase point sand bar body, tidal flat phase sand bar and marine-continental transition phase delta.

[0082] Specifically, refer to Figure 1 As shown in step S121, if the morphology of the target geological body is a fluvial sand body, the length and width of the sand body distribution in the fluvial sand body are determined; the correlation between the sedimentary microfacies and the well seismic data is determined by the sedimentary microfacies spatial variation function of the non-fixed range composed of the length and width.

[0083] Among them, river-facies sand bodies are one of the important reservoir types of oil and gas reservoirs. However, the frequent diversion of rivers causes the thickness of river-facies sand bodies to be uneven and the temporal and spatial distribution to be complex. At the same time, the seismic attributes are restricted by the resolution of seismic data and cannot simply meet the developers' requirements for the accuracy of thin interbedded sand body characterization. Figure 2 As shown in the figure, for some relatively straight rivers, channel deposits and sand bar deposits are basically integrated, the sand body distribution is characterized by a single-direction distribution, and there are muddy barriers between the channels. In this case, the basic width of the long channel should be calculated ( Figure 2 The middle arrow indicates the width) and the approximate extension distance of the river ( Figure 2 The extension direction of the middle dotted line is the extension distance), which is taken as the influence range of the logging data. In this way, the simulation results are better in distinguishing different channel sand bodies.

[0084] Reference Figure 1 As shown in step S122, if the morphology of the target geological body is a fluvial phase point sand bar body or a tidal flat phase sand bar, the major variation range and minor variation range of the sand body distribution of the fluvial phase point sand bar body or the tidal flat phase sand bar are determined; the correlation between the sedimentary microfacies and the well seismic data is determined by the sedimentary microfacies spatial variation function of the non-fixed range composed of the major variation range and the minor variation range.

[0085] Combination Figure 3 As shown in Figure 2, for some river deposits with high tortuosity, point-bar sand body deposits are the main oil and gas enrichment areas. When simulating them, the nearly fan-shaped (or semi-elliptical) sedimentary bodies formed by point-bar sand bodies are mainly considered (refer to Figure 3 The semi-ellipse should be used as the range of the variable influence ratio.

[0086] Combination Figure 4 As shown, for tidal flat sand bars (refer to Figure 4The proportional relationship between its length and width can be obtained through measurement, and the main and secondary directions of the sand body can be displayed. Based on this, the influence range of the logging data can be changed into an elliptical area formed by the lengths of these two directions. In this way, the logging data within this range have geological relevance, and the spatial distribution characteristics of the geological body can also be portrayed in the simulation results.

[0087] Reference Figure 1 As shown in step S123, if the morphology of the target geological body is a marine-continental transition delta, the distribution range of the front sand body and / or coastal sand body in the marine-continental transition delta is determined, and the correlation between the sedimentary microfacies and the well seismic data is determined by the sedimentary microfacies spatial variation function of the range.

[0088] Combination Figure 5 As shown, there are many good oil-storing rock sandstone bodies in deltaic deposits. For example, the sheet sand at the front of the estuary bar of the river-controlled delta and the sand of the distributary channel; the beach sand and barrier sand bar in the wave-controlled delta all have good oil storage properties. Among them, the distributary channel sandstone is generally not as favorable as other sand bodies because it is far away from the oil source area. Therefore, in ancient deltaic deposits, the main reservoirs are the delta front sand and the coastal sand that is closely associated with the delta destruction. There, the pre-delta mud adjacent to the delta front sand is finger-like and interspersed, thus forming a composite reservoir and forming good conditions for oil and gas accumulation. This is a typical feature of many large oil and gas fields at the delta front. However, in the deltaic system, along the Lower Wilcox oil-producing structural belt, many large structures are still non-oil-producing. This is because they are in the bay mud deposits of inter-deltaic sandstone-poor. The inventors of the present invention have classified the distribution range of the front sand body and / or coastal sand body in the marine-continental transitional phase delta (refer to Figure 5 The area enclosed by the dotted circle in the middle is shown as the range of the variable influence ratio that affects the well seismic data.

[0089] Of course, the embodiments of the present invention are not limited to the above-mentioned heterogeneous sedimentary geological bodies. Examples of other heterogeneous sedimentary geological bodies are not listed in detail in the present invention. The correlation between sedimentary microfacies and well seismic data is also determined by using a non-variable sedimentary microfacies spatial variation function, and the embodiments of the present invention are not repeated here.

[0090] In a specific embodiment, in view of the main shortcomings of the Junior influence ratio algorithm of well-seismic combination in the prior art, the present invention provides a multi-point statistical modeling method for oil and gas reservoir areas, which realizes that the influence ratio at each grid node inside the space can be changed with the different positions of each simulated node relative to the well, thereby improving the effect of well-seismic combination modeling and providing technical support for further exploration of oil and gas reservoirs. The theoretical algorithm in the embodiment of the present invention can be combined with the theoretical algorithm in the patent application with publication number CN103678899A and invention name "A multi-point statistical modeling method and device for oil and gas reservoirs based on variable influence ratio", which will not be repeated in this embodiment.

[0091] Reference Figure 6 As shown, the above-mentioned multi-point statistical modeling method for oil and gas reservoir regions may specifically include the following steps:

[0092] Step S61: Collect well logging data and seismic data corresponding to the three-dimensional current oil and gas reservoir area.

[0093] Step S62: setting a three-dimensional rectangular grid based on the spatial positions of the well logging data and the seismic data.

[0094] Step S63: assign the well logging data and seismic data of the oil and gas reservoir area to the corresponding grid nodes of the three-dimensional rectangular grid according to the locations where the well logging data and seismic data are taken.

[0095] Step S64, determining the morphology of the target geological body contained in the target sedimentary phase in the oil and gas reservoir area; if the morphology of the target geological body is a heterogeneous sedimentary geological body, executing step S65; if the morphology of the target geological body is a homogeneous sedimentary geological body, executing step S66.

[0096] The target geological bodies described in geological modeling are mostly irregular in shape. Taking the marine-continental transitional sand bar as an example, its shape is mainly long-axis, and the axial direction is the same as the direction of the ancient water flow. If the influence range of the well logging data is only specified as a circle, the long-axis geological body of the sand bar may cause some of the input well logging data points to be out of the same influence range. In this case, if the drilling density is high, the impact on the results is small. However, in most areas with low drilling density or irregular well network, it will have a greater impact on the results, resulting in inaccurate final simulation results. The simulation results of the geological body are biased towards a circle and cannot reflect the heterogeneity of the geological body in different directions. Based on the understanding of the sedimentary geology of the simulated target sand body, this method takes the tide-controlled sand bar as an example. The statistically significant ratio of length and width can be obtained, which is divided into the main direction and secondary direction of the sand body distribution. Based on this, the influence range of the well logging data is changed to the elliptical area formed by the length of these two directions. In this way, the logging data within this range will have geological relevance, and the spatial distribution characteristics of the geological body will be better portrayed in the simulation results.

[0097] Step S65, dividing the three-dimensional rectangular grid into a first area and a second area according to the non-fixed variation range of the sedimentary microfacies spatial variation function.

[0098] In this embodiment, a tidal flat sand bar is used as an example for explanation. The distribution direction of the sand body of the sand bar is the main direction and the secondary direction. The corresponding length R1 and width R2 are used. The basic principles of geostatistics are used to calculate the non-fixed range of the sedimentary microfacies spatial variation function. Where R1>R2, then R1 is the main range and R2 is the secondary range. This step uses the range of the sedimentary microfacies spatial variation function to divide the grid nodes of the entire study area into two parts. The first part is centered on each well location, with the ranges R1 and R2 being the major axis radius and minor axis radius of the ellipse respectively. The second part is the area remaining after removing the first part from the entire study area.

[0099] Reference Figure 7 As shown in the figure, each point on the ellipse represents the range of the reservoir traversal function formed in the direction between the point and the origin. The range is caused by the anisotropy of the sand bar reservoir properties, and the main range is greater than the secondary range. The ranges in all directions form an ellipse, whose main range coincides with the X-axis and the secondary range coincides with the Y-axis. Figure 8 As shown, Figure 8 The main variation range of the sand bar shown in does not coincide with the X-axis, but exists at a certain angle with the X-axis. At the same time, the secondary variation range does not coincide with the Y-axis. Under the influence of the anisotropy of the target geological body, the variation range of the corresponding variation function of the sedimentary microfacies space in this embodiment will be different with the change of direction.

[0100] Therefore, in this embodiment, only when there is no anisotropy of reservoir properties and the ranges of the variograms obtained in all directions are consistent, can the same range be truly used. Under the premise of anisotropy, the range of the corresponding variogram will be different with the change of direction. Any point in the ellipse in the embodiment of the present invention will have a certain correlation with the value of the reservoir variable at the center point of the ellipse, and the reservoir variable at any point outside the ellipse will not have a correlation with the value of the reservoir variable at the center point of the ellipse.

[0101] Step S66, dividing the three-dimensional rectangular grid into a first area and a second area according to the fixed variation range of the sedimentary microfacies spatial variation function.

[0102] Reference Fig. 9 and Fig.10 As shown, when the target geological body is a homogeneous sedimentary geological body, the existence of the circle means that the variation range is a constant in each direction, representing the isotropy of the homogeneous sedimentary geological body.

[0103] In a specific implementation, the basic principles of geostatistics can be used to calculate the range R of the spatial variation function of the sedimentary microfacies. As an important parameter of the variation function, the range R can be used to describe the influence range of the well logging data. When the distance between the spatial grid point and a well is greater than the range, the sedimentary microfacies at the grid point is not related to the well logging data of the well and is not affected by the well logging data. When this distance is less than the range, the sedimentary microfacies at the grid point is affected by the well logging data of the well.

[0104] This step uses the range of the sedimentary microfacies spatial variation function to divide the grid nodes of the entire study area into two parts. The first part is the area with each well location as the center and the range R as the radius. The second part is the area left after removing the first part from the entire study area.

[0105] The intersections of the east-west grid lines and the north-south grid lines in the study area are the grid nodes. These grid nodes are the spatial locations where simulations need to be performed. Fig.10 As shown in the figure, the shaded circular part is the first area, the central circle indicates the location of the well, and the remaining part is the second area. The radius of this circle is the range of the sedimentary microfacies variation function. The two shaded circles in the figure represent the areas corresponding to the two wells represented by the central circle color in the area.

[0106] Step S67: Determine the variable influence ratio of the grid nodes in the oil and gas reservoir area.

[0107] The present invention takes into account the spatial distribution of microfacies anisotropy of the reservoir. When the estimated point is taken as the center, the main direction corresponding to the X direction and the secondary direction corresponding to the Y direction, as well as the main range and secondary range generated thereby, are calculated. According to the corresponding description in the geostatistical literature, and the ellipse composed of the ranges obtained in these two different directions (such as Figure 7 and Figure 8 shown), can be substituted for Fig.10 The first part of the division (such as Fig.10 The circle shown in ) is used to complete the variable influence ratio algorithm of multi-point modeling combined with well seismic. Each grid node inside the ellipse defined in this way is correlated with the microfacies obtained from the logging data at the point to be estimated. Therefore, in the constant influence ratio algorithm, when simulating each point, all the logging data inside the ellipse should be taken into account, and the logging data outside the ellipse should not be taken into account.

[0108] Specifically, the implementation of this step may include: determining whether the grid node falls within the first area; if so, the variable influence ratio is dominated by the logging data; otherwise, the variable influence ratio is dominated by the seismic data.

[0109] In the process of well-seismic combined modeling, when the simulated point falls in the second area, because the distance from the well point exceeds the range, it is not related to each well point, so the simulation is mainly based on seismic data. When the simulated point falls in the first area, because the distance from the well point is within the range of the range, it has a certain correlation with these well points, so the simulation is mainly based on logging data.

[0110] For the first simulated area, the well logging data has the main influence, and the influence of seismic data is very small, so the influence ratio is dominated by the well logging data. For the second area of ​​the area, since the influence of well logging is relatively small and the influence of seismic data is relatively large, the influence ratio is dominated by seismic data. In this way, the influence ratio can change with the different positions of each simulated spatial node, which makes up for the shortcomings of the Junior algorithm.

[0111] Step S68: Perform spatial modeling according to the variable influence ratio.

[0112] Step S69: determine the oil and gas reservoir layer according to the multi-point statistical spatial modeling results corresponding to the oil and gas reservoir area.

[0113] In a more specific embodiment, referring to Fig.11 As shown, the relationship between the main range, the secondary range and the ellipse formed by the simulation point as the center, and the circle with the fixed range (main range length) as the radius. This embodiment shows a circle with a main range of 730 meters as the radius, and an ellipse with a main range of 730 meters as the major axis and a secondary range of 553 meters as the minor axis. The three circles in the ellipse represent the logging data inside the ellipse, and the six cross marks represent the six logging data points outside the ellipse and inside the circle. There are two more logging data points outside the circle (shown as diamonds in the figure).

[0114] According to the algorithm design of the present invention, Fig.11 When simulating the coordinate origin in the figure, it is based on the three logging data represented by the circle inside the red ellipse, but not on the logging data represented by the cross symbol, let alone the two logging data represented by the diamond symbol.

[0115] However, in the patent with publication number CN103678899A previously proposed by the inventor of the present invention, the logging data used include not only the logging data represented by the three circles, but also the six logging data represented by the crosses, and only the two logging data represented by the diamond symbols cannot be used. The above difference is the improvement of the algorithm of the present invention relative to the algorithm of the patent with publication number CN103678899A. It can be seen that the algorithm of the present invention makes the logging data used to simulate a point meet a stricter correlation when the anisotropy of the reservoir parameters exists.

[0116] The three logging data points in the ellipse with the major and minor axes of the major and minor ranges mentioned in the present invention are related to the coordinate origin being simulated, and these logging data points can be used to simulate the reservoir parameters of the coordinate origin. However, for those logging data in the cross symbol with the major range as the radius, but not inside the ellipse, there is no correlation between the logging data and the points at the origin, so they cannot be used to simulate the coordinate origin.

[0117] <Comparison of modeling results>

[0118] Reference Fig.12 , Fig.13 and Fig.14 As shown, Fig.12 This is a schematic diagram of seismic data for the oil and gas reservoir area. Fig.13 and Fig.14 is the geological model of the area, where Fig.13 For a geological model constructed using variable influence ratios determined using a fixed range, Fig.14 The geological model is constructed using a variable influence ratio determined by a non-fixed range. Seismic data has advantages in expressing the macro characteristics of reservoir spatial distribution, but the influence range of the well logging curve must be considered when expressing smaller scales. As shown in Figure 150, there is a slight change in the seismic attributes between Well 150 and Well 158. If the sedimentary phase changes are not considered and only the regular logging influence range is used for lithological simulation, the sand bodies between the two wells are likely to be discontinuous. However, if the sedimentary microfacies characteristics of the sand bodies are considered, the distribution range of the sand bar in the study area shows that the two wells should be in the same sand bar development range in the direction of the profile, and the final fitting effect is also good, and the sand bodies of the two wells are very continuous.

[0119] Based on the same inventive concept, the embodiment of the present invention also provides a multi-point statistical modeling device for oil and gas reservoir regions, referring to Fig.15 As shown, the device may include: a collection module 11, a setting module 12, an assignment module 13, a judgment module 14, a division module 15, a determination module 16 and a modeling module 17, and its working principle is as follows:

[0120] The acquisition module 11 is used to acquire the well logging data and seismic data corresponding to the three-dimensional current oil and gas reservoir area;

[0121] A setting module 12, for setting a three-dimensional rectangular grid based on the spatial positions of the well logging data and the seismic data;

[0122] An assignment module 13, for assigning the well logging data and seismic data of the oil and gas reservoir area to corresponding grid nodes of the three-dimensional rectangular grid according to the locations where the well logging data and the seismic data are taken;

[0123] The judgment module 14 is used to judge the morphology of the target geological body contained in the target sedimentary facies; if the judgment module judges that the morphology of the target geological body is a heterogeneous sedimentary geological body, it is used to determine the correlation between the sedimentary microfacies and the well seismic data using the sedimentary microfacies spatial variation function with a non-fixed range.

[0124] A division module 15, for dividing the three-dimensional rectangular grid into a first area and a second area according to the non-fixed variation range of the sedimentary microfacies spatial variation function;

[0125] The determination module 16 is further used to determine the variable influence ratio of the grid nodes in the oil and gas reservoir area;

[0126] The modeling module 17 is used to perform spatial modeling according to the variable influence ratio.

[0127] The determination module 16 is further used to determine the oil and gas reservoir layer according to the multi-point statistical space modeling results corresponding to the oil and gas reservoir area.

[0128] In an optional embodiment, the heterogeneous sedimentary geological body includes at least one of the following: fluvial sand body, fluvial point sand bar body, tidal flat sand bar and marine-continental transitional delta.

[0129] In another optional embodiment, the judgment module 14 is specifically used to: if the morphology of the target geological body is a fluvial facies sand body, determine the length and width of the sand body distribution in the fluvial facies sand body; determine the correlation between the sedimentary microfacies and the well seismic data by using the sedimentary microfacies spatial variation function of the non-fixed range composed of the length and the width;

[0130] If the target geological body is in the form of a fluvial facies point sand bar or a tidal flat facies sand bar, determine the major variation range and minor variation range of the sand body distribution of the fluvial facies point sand bar or the tidal flat facies sand bar; determine the correlation between the sedimentary microfacies and the well seismic data using the sedimentary microfacies spatial variation function of the non-fixed variation range composed of the major variation range and the minor variation range;

[0131] If the morphology of the target geological body is a marine-continental transition delta, the distribution range of the front sand body and / or coastal sand body in the marine-continental transition delta is determined, and the correlation between the sedimentary microfacies and the well seismic data is determined by the sedimentary microfacies spatial variation function of the range.

[0132] In another optional embodiment, the determination module 16 is specifically used to determine whether the grid node falls within the first area; if so, the variable influence ratio is dominated by logging data; otherwise, the variable influence ratio is dominated by seismic data.

[0133] In another optional embodiment, the above-mentioned judgment module 14 is also used to determine the correlation between sedimentary microfacies and well seismic data using a sedimentary microfacies spatial variation function with a fixed range if the target geological body is a homogeneous sedimentary geological body; correspondingly, the division module 15 is used to divide the three-dimensional rectangular grid into a first area and a second area according to the fixed range of the sedimentary microfacies spatial variation function.

[0134] Based on the same inventive concept, embodiment 1 of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the above-mentioned multi-point statistical modeling method for oil and gas reservoir areas is implemented.

[0135] Based on the same inventive concept, embodiment 1 of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned multi-point statistical modeling method for oil and gas reservoir areas when executing the program.

[0136] It should be noted that since the principles of solving the problems by these devices, computer-readable storage media and computer equipment are similar to those of the aforementioned methods, the implementation of the devices, computer-readable storage media and computer equipment can refer to the implementation of the aforementioned methods and will not be elaborated here.

[0137] Example 2

[0138] Embodiment 2 of the present invention provides a reservoir distribution prediction method, which comprises: predicting reservoir distribution according to the geological model obtained by the multi-point statistical modeling method of the oil and gas reservoir area described in Embodiment 1.

[0139] The reservoir distribution prediction method in the embodiment of the present invention uses the geological model obtained in the embodiment 1 as reference data for reservoir distribution prediction to perform reservoir distribution prediction. Specific examples thereof will not be repeated here.

[0140] Based on the same inventive concept, embodiment 2 of the present invention further provides a reservoir distribution prediction device, which may include: a prediction module and a multi-point statistical modeling device for oil and gas reservoir regions in embodiment 1;

[0141] The prediction module is used to predict the reservoir distribution according to the geological model obtained by the multi-point statistical modeling device based on the oil and gas reservoir area.

[0142] Based on the same inventive concept, Embodiment 2 of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the above-mentioned reservoir distribution prediction method is implemented.

[0143] Based on the same inventive concept, Embodiment 2 of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned reservoir distribution prediction method when executing the program.

[0144] It should be noted that since the principles of solving the problems by these devices, computer-readable storage media and computer equipment are similar to those of the aforementioned method, the implementation of the devices, computer-readable storage media and computer equipment can refer to the implementation of the aforementioned method, and the specific manner in which each module in the reservoir distribution prediction device performs operations has also been described in detail in the embodiments of the method, and will not be elaborated here.

[0145] Example 3

[0146] Embodiment 3 of the present invention provides a method for analyzing oil reservoir migration, which comprises: analyzing oil reservoir migration according to the geological model obtained by the multi-point statistical modeling method of the oil and gas reservoir area described in Embodiment 1.

[0147] The above-mentioned reservoir distribution prediction method in the embodiment of the present invention uses the geological model obtained in the above-mentioned embodiment 1 as reference data for reservoir migration analysis to predict reservoir distribution. The specific examples thereof are not repeated here.

[0148] Based on the same inventive concept, embodiment 3 of the present invention further provides an oil reservoir migration analysis device, which may include: a migration analysis module and the oil and gas reservoir region multi-point statistical modeling device in embodiment 1;

[0149] The migration analysis module is used to analyze the migration of oil reservoirs according to the geological model obtained by the multi-point statistical modeling device of the oil and gas reservoir area.

[0150] Based on the same inventive concept, Embodiment 3 of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the above-mentioned reservoir migration analysis method is implemented.

[0151] Based on the same inventive concept, Embodiment 3 of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned reservoir migration analysis method when executing the program.

[0152] It should be noted that since the principles of solving the problems by these devices, computer-readable storage media and computer equipment are similar to those of the aforementioned methods, the implementation of the devices, computer-readable storage media and computer equipment can refer to the implementation of the aforementioned methods and will not be elaborated here.

[0153] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0154] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0155] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0156] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps for the functions specified in one or more boxes.

[0157] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A multi-point statistical modeling method for oil and gas reservoir areas, It is characterized in that include: Determine the morphology of the target geological body contained in the target sedimentary phase in the oil and gas reservoir area; If the target geological body is a heterogeneous sedimentary geological body, and the target geological body is a fluvial sand body, the length and width of the sand body distribution in the fluvial sand body are determined; the correlation between the sedimentary microfacies and the well seismic data is determined by the sedimentary microfacies spatial variation function of the non-fixed range composed of the length and the width; If the morphology of the target geological body is a heterogeneous sedimentary geological body, and the morphology of the target geological body is a fluvial facies point sand bar body or a tidal flat facies sand bar, determine the major variation range and minor variation range of the sand body distribution of the fluvial facies point sand bar body or the tidal flat facies sand bar; determine the correlation between the sedimentary microfacies and the well seismic data by the sedimentary microfacies spatial variation function of the non-fixed range composed of the major variation range and the minor variation range; If the morphology of the target geological body is a heterogeneous sedimentary geological body, and the morphology of the target geological body is a marine-continental transitional delta, the distribution range of the front sand body and / or the coastal sand body in the marine-continental transitional delta is determined, and the correlation between the sedimentary microfacies and the well-seismic data is determined using the sedimentary microfacies spatial variation function with the range as a non-fixed range.

2. The method according to claim 1, It is characterized in that Also includes: Collect well logging data and seismic data corresponding to the current three-dimensional oil and gas reservoir area; Setting a three-dimensional rectangular grid based on the spatial positions of the well logging data and the seismic data; According to the locations where the well logging data and the seismic data are taken, the well logging data and the seismic data of the oil and gas reservoir area are assigned to corresponding grid nodes of the three-dimensional rectangular grid; Dividing the three-dimensional rectangular grid into a first area and a second area according to the non-fixed variation range of the sedimentary microfacies spatial variation function; determining variable influence ratios of grid nodes in the oil and gas reservoir region; Spatial modeling is performed based on the variable influence ratios.

3. The method according to claim 2, It is characterized in that After performing spatial modeling according to the variable influence ratio, the method further includes: The oil and gas reservoir layer is determined according to the multi-point statistical spatial modeling results corresponding to the oil and gas reservoir area.

4. The method according to claim 3, It is characterized in that Determining the variable influence ratio of the grid nodes in the oil and gas reservoir region includes: Determining whether the grid node falls within the first area; If so, the variable influence ratio is dominated by the well logging data; Otherwise, the variable influence ratio is dominated by the seismic data.

5. The method according to any one of claims 1 to 4, It is characterized in that Also includes: If the target geological body is a homogeneous sedimentary geological body, the correlation between the sedimentary microfacies and the well seismic data is determined by the sedimentary microfacies spatial variation function with a fixed range; accordingly, The three-dimensional rectangular grid is divided into a first area and a second area according to the fixed variation range of the sedimentary microfacies spatial variation function.

6. A multi-point statistical modeling device for oil and gas reservoir areas, It is characterized in that include: A judgment module, used to judge the morphology of the target geological body contained in the target sedimentary phase; A determination module, if the determination module determines that the morphology of the target geological body is a heterogeneous sedimentary geological body, and the morphology of the target geological body is a fluvial facies sand body, the determination module is used to determine the length and width of the sand body distribution in the fluvial facies sand body; determine the correlation between the sedimentary microfacies and the well seismic data by using the sedimentary microfacies spatial variation function of the non-fixed range composed of the length and the width; If the morphology of the target geological body is a heterogeneous sedimentary geological body, and the morphology of the target geological body is a river phase point sand bar body or a tidal flat phase sand bar, the determination module is used to determine the main range and secondary range of the sand body distribution of the river phase point sand bar body or the tidal flat phase sand bar; the correlation between the sedimentary microfacies and the well seismic data is determined by the sedimentary microfacies spatial variation function of the non-fixed range composed of the main range and the secondary range; if the morphology of the target geological body is a heterogeneous sedimentary geological body, and the morphology of the target geological body is a marine-continental transitional phase delta, the determination module is used to determine the range of the distribution of the front sand body and / or the coastal sand body in the marine-continental transitional phase delta, and the correlation between the sedimentary microfacies and the well seismic data is determined by the sedimentary microfacies spatial variation function of the non-fixed range.

7. The device according to claim 6, It is characterized in that Also includes: The acquisition module is used to acquire the well logging data and seismic data corresponding to the current three-dimensional oil and gas reservoir area; A setting module, used for setting a three-dimensional rectangular grid based on the spatial positions of the well logging data and the seismic data; An assignment module, used for assigning the well logging data and seismic data of the oil and gas reservoir area to corresponding grid nodes of the three-dimensional rectangular grid according to the value locations of the well logging data and the seismic data; A division module, used for dividing the three-dimensional rectangular grid into a first area and a second area according to the non-fixed variation range of the sedimentary microfacies spatial variation function; The determination module is further used to determine the variable influence ratio of the grid nodes in the oil and gas reservoir area; A modeling module is used to perform spatial modeling according to the variable influence ratio.

8. A reservoir distribution prediction method, It is characterized in that include: The reservoir distribution is predicted according to the geological model obtained by the multi-point statistical modeling method for the oil and gas reservoir region according to any one of claims 1 to 5.

9. A reservoir distribution prediction device, It is characterized in that include: A prediction module and a multi-point statistical modeling device for oil and gas reservoir regions as claimed in claim 6 or 7; The prediction module is used to predict reservoir distribution based on the geological model obtained by the multi-point statistical modeling device of the oil and gas reservoir area.

10. A method for analyzing oil and gas reservoir migration, It is characterized in that include: The migration of oil and gas reservoirs is analyzed according to the geological model obtained by the multi-point statistical modeling method for oil and gas reservoir regions according to any one of claims 1 to 5.

11. An oil and gas reservoir migration analysis device, It is characterized in that include: A migration analysis module and a multi-point statistical modeling device for an oil and gas reservoir region as claimed in claim 6 or 7; The migration analysis module is used to analyze the migration of oil and gas reservoirs based on the geological model obtained by the multi-point statistical modeling device of the oil and gas reservoir area.

12. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the program is executed by a processor, it implements the oil and gas reservoir area multi-point statistical modeling method as described in any one of claims 1 to 5, or implements the reservoir distribution prediction method as described in claim 8, or implements the oil and gas reservoir migration analysis method as described in claim 10.

13. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, It is characterized in that When the processor executes the program, it implements the oil and gas reservoir area multi-point statistical modeling method as described in any one of claims 1 to 5, or implements the reservoir distribution prediction method as described in claim 8, or implements the oil and gas reservoir migration analysis method as described in claim 10.

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