Target sand body identification method and device based on sensitive parameters of water-sensitive reservoir

Abstract

The invention relates to the technical field of oil-gas exploration and development, in particular to a target sand body identification method and device based on sensitive parameters of a water-sensitive reservoir. The method comprises the following steps: respectively establishing relation charts among clay data, logging data and water-sensitive data, and determining optimal clay minerals and sensitive parameters according to the relation charts; according to the seismic data, using seismic waveform difference simulation or seismic waveform indication simulation to establish a simulation data volume of the sensitive parameters; determining a target sand body range according to the relation chart; and according to the target sand body range and the simulation data volume of the sensitive parameters, establishing a target sand body three-dimensional model, and completing target sand body identification. The problems that an existing reservoir prediction method is low in longitudinal resolution or is limited to lithology and oil layer prediction, and reservoir target sand body prediction cannot be achieved under the condition of double constraints of oiliness and water sensitivity degree are solved.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development technology, and in particular to a method and apparatus for identifying target sand bodies based on water-sensitive reservoir sensitive parameters. Background Technology

[0002] With the continuous improvement of oil and gas exploration, finding new replacement oil and gas resources has become one of the hot issues in oil and gas exploration today. At present, some reservoirs in oil fields are characterized by scattered planar distribution, high clay mineral content, strong water sensitivity, and uneven distribution of water sensitivity. The development of such reservoirs is more difficult, and it is necessary to make accurate target sand body predictions for such reservoirs in advance.

[0003] Existing methods for reservoir prediction related to earthquakes include: First, reservoir prediction using seismic attribute slices. The disadvantage of this method is its low vertical resolution, which limits its application to thin reservoirs with many layers and small vertical spacing. Second, reservoir prediction using a combination of well-seismic data and impedance inversion or well logging curve simulation. This method can make full use of the advantages of high vertical resolution of well data and dense lateral sampling of seismic data, but it is limited to the prediction of lithology or oil layers.

[0004] Currently, there is no method to predict target sand bodies under the dual constraints of oil content and water sensitivity. Summary of the Invention

[0005] This invention proposes a target sand body identification method and device based on water-sensitive reservoir sensitive parameters to solve the problem that existing reservoir prediction methods have low vertical resolution or are limited to predicting lithology and oil layers, and cannot achieve reservoir target sand body prediction under the dual constraints of oil content and water sensitivity.

[0006] According to one aspect of the present invention, a method for identifying target sand bodies based on water-sensitive reservoir sensitive parameters is provided, comprising:

[0007] Acquire seismic data, well logging data, clay data, and water-sensitive data for the work area;

[0008] Relationship charts were established between clay data, well logging data, and water-sensitive data. Based on these charts, the optimal clay mineral with the best correlation to water-sensitive data in the clay data was determined, and the sensitive parameter with the best correlation to oil content in the well logging data was determined.

[0009] Based on the earthquake data, a simulation data volume of the sensitive parameters is established using earthquake waveform difference simulation or earthquake waveform indication simulation.

[0010] The target sand body range is determined based on the aforementioned relational diagram;

[0011] Based on the target sand body range and the simulated data of the sensitive parameters, a three-dimensional model of the target sand body is established to complete the target sand body identification.

[0012] Preferably, the seismic data includes at least: post-stack seismic data volume and seismic interpretation horizons.

[0013] Preferably, the logging data includes at least: acoustic, density, gamma, spontaneous potential, and resistivity logging curves.

[0014] Preferably, the clay data includes at least the absolute contents of montmorillonite, illite, kaolinite, chlorite, illite-montmorillonite mixed layers, chlorite-montmorillonite mixed layers, and total clay minerals in the target layer.

[0015] Preferably, before establishing the relationship charts between clay data, well logging data, and water-sensitive data, the core depth is corrected. The method includes:

[0016] By comparing the drill string depth with the electrical logging depth of the standard core layer, the difference between the two is determined. The drill string depth is then corrected based on the electrical logging depth to obtain the corrected core depth.

[0017] Preferably, before establishing the relationship charts between clay data, well logging data, and water-sensitive data, the well logging data is preprocessed, and the method includes:

[0018] The acoustic, density, gamma, and resistivity logging curves are processed for outliers and standardized.

[0019] The spontaneous potential logging curves are subjected to outlier processing, baseline correction, and standardization.

[0020] Preferably, the method for establishing a relationship chart between clay data, well logging data, and water-sensitive data, and determining the optimal clay mineral in the clay data that has the best correlation with the water-sensitive data based on the relationship chart, includes:

[0021] Establish graphs showing the relationship between the absolute content of each clay mineral and the total clay mineral content in the clay data and the water-sensitive data, and determine the clay mineral with the best correlation with the water-sensitive data as the optimal clay mineral.

[0022] Preferably, the method for establishing a relationship chart between clay data, well logging data, and water-sensitive data, and determining the sensitive parameter in the well logging data that has the best correlation with the optimal clay mineral and oil-bearing properties based on the relationship chart, includes:

[0023] The sensitive parameters include: a first sensitive parameter and a second sensitive parameter;

[0024] Establish charts showing the relationship between each logging curve and the absolute content of the optimal clay mineral in the logging data, and determine the logging curve with the best correlation to the optimal clay mineral as the first sensitive parameter;

[0025] Establish charts showing the relationship between each logging curve and the first sensitive parameter and oil content in the logging data, and determine the logging curve with the best correlation with the first sensitive parameter and oil content as the second sensitive parameter.

[0026] Preferably, the method for determining the target sand body range based on the relational map includes:

[0027] Select the target water sensitivity and oil content;

[0028] Based on the range of water-sensitive data values ​​corresponding to the target water sensitivity level, the range of target sand bodies that simultaneously meet the requirements of the target water sensitivity level and oil content is determined on the relationship chart.

[0029] Preferably, the range of water-sensitive data values ​​corresponding to the water sensitivity level includes:

[0030] The water sensitivity levels include: no water sensitivity, weak water sensitivity, moderately weak water sensitivity, moderately strong water sensitivity, strong water sensitivity, and extremely strong water sensitivity.

[0031] The water sensitivity data Wsd corresponding to the water sensitivity level is within the following ranges: when Wsd ≤ 0.05, it is no water sensitivity; when 0.05 < Wsd ≤ 0.3, it is weak water sensitivity; when 0.3 < Wsd ≤ 0.5, it is moderately weak water sensitivity; when 0.5 < Wsd ≤ 0.7, it is moderately strong water sensitivity; when 0.7 < Wsd ≤ 0.9, it is strong water sensitivity; and when 0.9 < Wsd, it is extremely strong water sensitivity.

[0032] Preferably, the method for establishing a simulation data volume of the sensitive parameter based on the seismic data using seismic waveform difference simulation or seismic waveform indication simulation includes:

[0033] Using the seismic data and the well logging data, a composite seismic record is created;

[0034] Based on the earthquake data, the top and bottom of the target layer and the stratigraphic interfaces of groups and segments within the target layer are selected to establish a framework model of the target layer in the work area.

[0035] The optimal number of samples is determined by waveform indication method;

[0036] Determine the optimal cutoff frequency value for the model based on the cutoff frequency calculation formula;

[0037] Based on the aforementioned framework model, seismic data, well logging data, seismic synthetic records, optimal sample size, and optimal cutoff frequency, a simulated data volume of sensitive parameters is obtained using seismic waveform difference simulation or seismic waveform indication simulation.

[0038] Preferably, the method for determining the optimal number of samples using the waveform indication method includes:

[0039] Different numbers of samples are selected using waveform indication methods;

[0040] Estimate the corresponding prediction curves based on different numbers of samples;

[0041] Correlation analysis was performed between the predicted curve and the original curve to obtain the best-fit sample chart;

[0042] When the correlation coefficient in the best-fit sample chart no longer changes or the change range is below a predetermined range, the number of samples corresponding to the correlation coefficient at this time is the optimal number of samples.

[0043] Preferably, the method further includes: extracting the target well placement layer sand body from the target sand body three-dimensional model, and deploying development horizontal wells using the target well placement layer sand body.

[0044] According to one aspect of the present invention, a target sand body identification device based on water-sensitive reservoir sensitive parameters is provided, comprising:

[0045] The acquisition unit is used to acquire seismic data, well logging data, clay data, and water-sensitive data of the work area;

[0046] The relationship chart establishment unit is used to establish relationship charts between clay data, well logging data and water-sensitive data respectively. Based on the relationship charts, the best clay mineral with the best correlation with water-sensitive data in the clay data is determined, and the sensitive parameter with the best correlation with oil-bearing properties in the well logging data is determined.

[0047] The simulation data volume establishment unit is used to establish the simulation data volume of the sensitive parameters based on the seismic data by using seismic waveform difference simulation or seismic waveform indication simulation.

[0048] The target sand body range determination unit is used to determine the range of the target sand body based on the relational map.

[0049] The model building unit is used to build a three-dimensional model of the target sand body based on the target sand body range and the simulated data volume of the sensitive parameters, thereby completing the target sand body identification.

[0050] The present invention has at least the following beneficial effects:

[0051] This invention proposes a method and device for identifying target sand bodies based on water-sensitive reservoir parameters. By making full use of highly accurate clay and water-sensitive data, combined with high-resolution vertical logging data and seismic data with dense lateral sampling, the sensitive parameters of the target sand body are identified. A three-dimensional model of the target sand body is established by combining well and seismic data, thereby accurately and quickly finding high-quality reservoirs with good oil content and relatively weak water sensitivity, providing strong support for stable oilfield production. Attached Figure Description

[0052] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present invention and, together with the specification, serve to explain the technical solutions of the present invention.

[0053] Figure 1 A flowchart illustrating target sand body identification based on water-sensitive reservoir sensitive parameters according to an embodiment of the present invention is shown;

[0054] Figure 2 A graph showing the relationship between the absolute content of total clay minerals and water-sensitive data according to an embodiment of the present invention;

[0055] Figure 3 A graph showing the relationship between gamma logging data and the absolute content of total clay minerals according to an embodiment of the present invention;

[0056] Figure 4 This illustrates a dual-constraint chart of gamma and resistivity logging data according to an embodiment of the present invention;

[0057] Figure 5 A cross-section of a gamma simulation data volume according to an embodiment of the present invention is shown;

[0058] Figure 6 A volumetric profile of resistivity simulation data is shown according to an embodiment of the present invention;

[0059] Figure 7 A three-dimensional model of the target sand body according to an embodiment of the present invention is shown;

[0060] Figure 8 This illustrates a target sand body profile with seismic waveform overlay according to an embodiment of the present invention;

[0061] Figure 9 This diagram illustrates the drilling effect of a small layer of a target sand body according to an embodiment of the present invention.

[0062] Figure 10 The optimal fit sample quality control graph is shown according to an embodiment of the present invention. Detailed Implementation

[0063] Various exemplary embodiments, features, and aspects of the present invention will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0064] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0065] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0066] Furthermore, to better illustrate the present invention, numerous specific details are set forth in the following detailed embodiments. Those skilled in the art will understand that the present invention can be practiced without certain specific details. In some instances, methods, means, elements, and circuits well known to those skilled in the art have not been described in detail in order to highlight the spirit of the invention.

[0067] Figure 1 A flowchart illustrating target sand body identification based on water-sensitive reservoir sensitive parameters according to an embodiment of the present invention is shown; Figure 2 A graph showing the relationship between the absolute content of total clay minerals and water-sensitive data according to an embodiment of the present invention; Figure 3 A graph showing the relationship between gamma logging data and the absolute content of total clay minerals according to an embodiment of the present invention; Figure 4 This illustrates a dual-constraint chart of gamma and resistivity logging data according to an embodiment of the present invention; Figure 5 A cross-section of a gamma simulation data volume according to an embodiment of the present invention is shown; Figure 6 A volumetric profile of resistivity simulation data is shown according to an embodiment of the present invention; Figure 7 A three-dimensional model of the target sand body according to an embodiment of the present invention is shown; Figure 8 This illustrates a target sand body profile with seismic waveform overlay according to an embodiment of the present invention; Figure 9 This diagram illustrates the drilling effect of a small layer of a target sand body according to an embodiment of the present invention. Figure 10 The diagram shows the best-fit sample quality control graph according to an embodiment of the present invention. Figure 1-10As shown, a target sand body identification method based on water-sensitive reservoir sensitive parameters includes: Step S01: Acquiring seismic data, well logging data, clay data, and water-sensitive data of the work area; Step S02: Establishing a relationship chart between clay data, well logging data, and water-sensitive data respectively; Based on the relationship chart, determining the best clay mineral in the clay data that has the best correlation with the water-sensitive data, and determining the sensitive parameter in the well logging data that has the best correlation with the best clay mineral and oil-bearing properties; Step S03: Based on the seismic data, establishing a simulated data volume of the sensitive parameter using seismic waveform difference simulation or seismic waveform indication simulation; Step S04: Determining the range of the target sand body based on the relationship chart; Step S05: Establishing a three-dimensional model of the target sand body based on the range of the target sand body and the simulated data volume of the sensitive parameter, thus completing the target sand body identification.

[0068] The target sand body identification method based on water-sensitive reservoir sensitive parameters provided in this embodiment of the invention specifically includes the following steps:

[0069] Step S01: Acquire seismic data, well logging data, clay data, and water-sensitive data for the work area.

[0070] In this invention, the seismic data includes at least: post-stack seismic data volume and seismic interpretation horizons.

[0071] In this invention, the logging data includes at least: acoustic, density, gamma, spontaneous potential, and resistivity logging curves.

[0072] In this invention, the clay data includes at least the absolute contents of montmorillonite, illite, kaolinite, chlorite, illite-montmorillonite mixed layer, chlorite-montmorillonite mixed layer, and total clay minerals in the target layer.

[0073] In this embodiment of the invention, the data acquired for the target work area includes at least seismic data, well logging data, clay data, and water-sensitive data. The seismic data includes preprocessed post-stack seismic data volumes and seismic interpretation horizons; the well logging data includes acoustic, density, gamma, spontaneous potential, and resistivity logging curves; the clay data includes the absolute contents of montmorillonite, illite, kaolinite, chlorite, illite-montmorillonite mixed layers, chlorite-montmorillonite mixed layers, and total clay minerals in the target layer; and the water-sensitive data is the water-sensitive index of the target layer. The clay data and water-sensitive data are obtained through laboratory experiments using actual formation cores.

[0074] Step S02: Establish relationship charts between clay data, well logging data, and water-sensitive data respectively. Based on the relationship charts, determine the best clay mineral in the clay data that has the best correlation with the water-sensitive data, and determine the sensitive parameters in the well logging data that have the best correlation with the best clay mineral and oil-bearing properties.

[0075] In this invention, before establishing the relationship chart between clay data, well logging data and water-sensitive data respectively, the core depth is corrected. The method includes: comparing the drill string depth of the standard layer of the core well with the electrical logging depth to determine the difference between the two, and correcting the drill string depth based on the electrical logging depth to obtain the corrected core depth.

[0076] In this embodiment of the invention, the drilling depth (depth obtained by drilling) of the standard layer of the core well in the work area is compared with the electrical logging depth (depth obtained by electrical logging) to find the difference between the two. The drilling depth is corrected based on the electrical logging depth, thereby correcting the core depth and obtaining the accurate core depth.

[0077] In this invention, before establishing the relationship charts between clay data, well logging data, and water-sensitive data, the well logging data is preprocessed. The method includes: performing outlier processing and standardization on the acoustic, density, gamma, and resistivity well logging curves; and performing outlier processing, baseline correction, and standardization on the spontaneous potential well logging curves.

[0078] In this embodiment of the invention, the preprocessing of the acoustic, density, gamma, and resistivity logging curves in the logging data includes outlier handling and standardization.

[0079] Preprocessing of spontaneous potential logging curves in well logging data includes outlier handling, baseline correction, and standardization.

[0080] Outlier handling involves replacing outliers with normal values ​​from the surrounding formation. Standardization begins with batch standardization, followed by fine-grained standardization. Baseline correction first identifies positive and negative anomalies in the spontaneous potential curve, then calculates and corrects the baseline values.

[0081] In this invention, the method of establishing a relationship chart between clay data, well logging data, and water-sensitive data, and determining the best clay mineral in the clay data that has the best correlation with the water-sensitive data based on the relationship chart, includes: establishing a relationship chart between the absolute content of each clay mineral and the total clay minerals in the clay data and the water-sensitive data, and determining the clay mineral that has the best correlation with the water-sensitive data as the best clay mineral.

[0082] In this embodiment of the invention, based on core experiment results, charts were established showing the relationship between the absolute contents of montmorillonite, illite, kaolinite, chlorite, illite-montmorillonite mixed layers, chlorite-montmorillonite mixed layers, and total clay minerals and water-sensitive data, respectively. The correlation R between each clay mineral and water-sensitive data was determined. 2 Among all the relationship diagrams, the total clay mineral with the best correlation to the water sensitivity index (data) is selected as the direct parameter for judging the degree of water sensitivity, and this total clay mineral is called the optimal clay mineral. For example... Figure 2 The figure shown is a chart illustrating the relationship between the absolute content of total clay minerals and water-sensitive data.

[0083] In this invention, the method for establishing relationship charts between clay data, well logging data, and water-sensitive data, and determining the sensitive parameter in the well logging data that has the best correlation with the optimal clay mineral and oil-bearing properties based on the relationship charts, includes: the sensitive parameter including a first sensitive parameter and a second sensitive parameter; establishing relationship charts between each well logging curve and the absolute content of the optimal clay mineral in the well logging data, and determining the well logging curve that has the best correlation with the optimal clay mineral as the first sensitive parameter; establishing relationship charts between each well logging curve in the well logging data, the first sensitive parameter, and oil-bearing properties, and determining the well logging curve that has the best correlation with the first sensitive parameter and oil-bearing properties as the second sensitive parameter.

[0084] In this embodiment of the invention, the method for determining the first sensitive parameter in the logging data is as follows: Establish a chart showing the relationship between the logging curves for acoustic wave, density, gamma, spontaneous potential, and resistivity and the optimal clay mineral, i.e., the absolute content of total clay minerals, and determine the correlation R between each logging curve and the absolute content of total clay minerals. 2 Among all relationship charts, the gamma-ray logging curve that best correlates with the optimal clay mineral is selected as the first sensitive parameter. For example... Figure 3 The figure shows the relationship between the selected first sensitive parameter, gamma logging curve, and the absolute content of total clay minerals. This gamma logging curve data can be used as an indirect parameter to determine the degree of water sensitivity.

[0085] The method for determining the second sensitive parameter in well logging data is as follows: Using the first sensitive parameter (gamma-ray logging data) as the X-axis, and the remaining logging curves (excluding those corresponding to the first sensitive parameter) as the Y-axis, with oil-bearing properties as the data point color, establish graphs showing the relationship between the remaining logging data (sonic, density, spontaneous potential, and resistivity logging curves) and the first sensitive parameter (gamma-ray logging curve), as well as oil-bearing properties. Combining this with the first sensitive parameter, determine the correlation between each logging curve (excluding the gamma-ray logging curve) and oil-bearing properties. From all the relationship graphs, select the resistivity logging curve with the best correlation to oil-bearing properties as the second sensitive parameter. Oil-bearing properties are obtained from laboratory core experiments. Figure 4 The figure shown is a chart showing the relationship between the selected second sensitive parameter, resistivity logging curve, gamma logging curve, and oil-bearing properties, i.e., a dual-constraint chart of gamma and resistivity logging data.

[0086] Step S03: Based on the earthquake data, establish a simulation data volume of the sensitive parameters using earthquake waveform difference simulation or earthquake waveform indication simulation.

[0087] In this invention, the method for establishing a simulated data volume of the sensitive parameter based on the seismic data using seismic waveform difference simulation or seismic waveform indication simulation includes: creating a seismic composite record using the seismic data and the well logging data; establishing a target layer framework model for the work area by selecting the top and bottom of the target layer and the stratigraphic interfaces of groups and sections within the target layer based on the seismic data; determining the optimal number of samples using a waveform indication method; determining the optimal cutoff frequency of the model according to the cutoff frequency calculation formula; and obtaining the simulated data volume of the sensitive parameter using seismic waveform difference simulation or seismic waveform indication simulation based on the framework model, seismic data, well logging data, seismic composite record, optimal number of samples, and optimal cutoff frequency.

[0088] In this invention, the method for determining the optimal number of samples using a waveform indication method includes: selecting different numbers of samples using a waveform indication method; estimating the corresponding prediction curves based on the different numbers of samples; performing correlation analysis on the prediction curves and the original curves to obtain a best-fit sample chart; when the correlation coefficient in the best-fit sample chart no longer changes or the change range reaches below a predetermined range, then the number of samples corresponding to the correlation coefficient at this time is the optimal number of samples.

[0089] In this embodiment of the invention, the simulation data volume of sensitive parameters is established by combining well seismic data, and the specific process includes:

[0090] A: By using seismic data and well logging data to create a detailed seismic composite record, the accurate depth location and seismic reflection characteristics of each main target layer on the seismic profile are determined, providing accurate time-depth relationships for the establishment of seismic waveform indication simulation and seismic waveform difference simulation models.

[0091] B: Using the seismic interpretation horizons in the seismic data, select the top and bottom of the target layer and the major stratigraphic boundaries (stratigraphic interfaces of groups and segments) within the target layer to establish a target layer framework model for the target work area.

[0092] C: By using waveform indication method, different numbers of samples are selected, where the samples are drilled sample well data similar to the target well in the work area.

[0093] Using the "best-fit sample" quality control chart, such as Figure 10 As shown, the optimal sample size is clearly defined. The horizontal axis of the "best-fit sample" quality control chart represents the number of samples, and the vertical axis represents the correlation index (correlation coefficient). The points in this chart are obtained by performing correlation analysis between the predicted curves estimated from different numbers of samples (optimized through waveform indicators) that have been drilled and applied to the original curves. For example, if the sample includes three wells, namely A, B, and C, the data from wells A and B are used to predict the curve for well C, resulting in a predicted curve. Then, correlation analysis is performed between the predicted curve for well C and the original curve obtained during actual drilling to obtain the best-fit sample chart.

[0094] Correlation analysis is performed on the predicted curves estimated based on different numbers of samples and the original curves to obtain the corresponding sample charts (quality control charts). In the sample charts, the correlation curve values ​​gradually increase. When the upward trend of the curve gradually flattens out, that is, the correlation coefficient no longer changes or the change is very small, the number of samples corresponding to the correlation coefficient at this time is determined to be the optimal number of samples.

[0095] The larger the number of valid samples, the lower the threshold for screening similar seismic waveforms; conversely, the smaller the number of valid samples, the higher the threshold for screening similar seismic waveforms. The optimal number of samples for the work area can be determined through chart analysis.

[0096] D: Calculate the optimal cutoff frequency for the model using the cutoff frequency calculation formula. The cutoff frequency calculation formula is as follows:

[0097] f_cutoff = v / 4d;

[0098] In the formula: f is the cutoff frequency, v is the formation velocity, and d is the minimum reservoir thickness.

[0099] The optimal cutoff frequency is the model's maximum frequency. A smaller cutoff frequency results in lower resolution but higher determinism, while a larger cutoff frequency results in higher resolution but higher randomness. The optimal cutoff frequency values ​​for the model are obtained through calculation and analysis, including the high-pass frequency and the high-cutoff frequency.

[0100] E: Under the control of the frame model, seismic data (post-stack seismic data volume), well logging data, seismic synthetic records, optimal sample number and optimal cutoff frequency, the simulation data volume of the first sensitive parameter and the simulation data volume of the second sensitive parameter are obtained by using the seismic waveform difference simulation or seismic waveform indication simulation method.

[0101] Step S04: Determine the range of the target sand body based on the aforementioned relational diagram.

[0102] In this invention, the method for determining the target sand body range based on the relational chart includes: selecting target water sensitivity and oil content; and determining, on the relational chart, the target sand body range that simultaneously meets the requirements of the target water sensitivity and oil content, based on the range of water sensitivity data values ​​corresponding to the target water sensitivity.

[0103] In this invention, the range of water sensitivity data corresponding to the water sensitivity level includes: the water sensitivity level includes: no water sensitivity, weak water sensitivity, moderately weak water sensitivity, moderately strong water sensitivity, strong water sensitivity, and extremely strong water sensitivity; wherein, the range of water sensitivity data Wsd corresponding to the water sensitivity level is: when Wsd≤0.05, it is no water sensitivity; when 0.05<Wsd≤0.3, it is weak water sensitivity; when 0.3<Wsd≤0.5, it is moderately weak water sensitivity; when 0.5<Wsd≤0.7, it is moderately strong water sensitivity; when 0.7<Wsd≤0.9, it is strong water sensitivity; when 0.9<Wsd, it is extremely strong water sensitivity.

[0104] In this embodiment of the invention, the target sand body range is determined by analyzing the relationship charts between the absolute content of clay minerals and water-sensitive data, the relationship charts between the first sensitive parameter and the absolute content of clay minerals, the relationship charts between the first sensitive parameter and the second sensitive parameter, and the relationship chart between oil content obtained in step S02.

[0105] That is, based on the range of water-sensitive data corresponding to the different water-sensitive levels mentioned above, we correspond them to the range of the absolute content of the optimal clay minerals in the relationship chart. Then, based on the range of the absolute content of the optimal clay minerals, we correspond them to the range of the first sensitive parameter in the relationship chart. Then, based on the range of the first sensitive parameter, we correspond them to the range of the second sensitive parameter. Finally, within the range of the first and second sensitive parameters in the chart, we find the range of the first and second sensitive parameters corresponding to the part that meets the oil content requirements. This is the final determined range of the target sand body.

[0106] For example, to define a water sensitivity level as "extremely high," where the range of values ​​is greater than 0.9, we would find the range of values ​​corresponding to this extreme water sensitivity (i.e., the range of values ​​for the absolute content of the optimal clay minerals greater than 0.9) on the optimal clay mineral absolute content relationship chart. Then, on the first sensitivity parameter relationship chart, we would find the range of values ​​for the first sensitivity parameter corresponding to this optimal clay mineral absolute content range. Finally, on the relationship chart between the first sensitivity parameter, the second sensitivity parameter, and oil content, we would find the range of values ​​for the second sensitivity parameter corresponding to this first sensitivity parameter range. Within the ranges constrained by the first and second sensitivity parameter ranges on this relationship chart, we would find the portion of the sand body that meets the oil content requirement. The values ​​of the first and second sensitivity parameters corresponding to this portion define the target sand body range.

[0107] Step S05: Based on the target sand body range and the simulated data of the sensitive parameters, establish a three-dimensional model of the target sand body to complete the target sand body identification.

[0108] In this invention, the method further includes: extracting the target well placement layer sand body from the three-dimensional model of the target sand body, and deploying development horizontal wells using the target well placement layer sand body.

[0109] In this embodiment of the invention, the target sand body range is determined jointly by the numerical range 'a' of the first sensitive parameter and the numerical range 'b' of the second sensitive parameter. The ranges of values ​​a and b are determined by... Figure 2 , Figure 3 and Figure 4 The diagram in the image has been determined.

[0110] A three-dimensional model of the target sand body is established by combining the target sand body range, the simulation data volume of the first sensitive parameter, and the simulation data volume of the second sensitive parameter.

[0111] The target sand body three-dimensional model is the common (overlapping) part of the a-value range of the first sensitive parameter simulation data volume and the b-value range of the second sensitive parameter simulation data volume.

[0112] Extract well-placement sand bodies with good continuity and large scale from the three-dimensional model of the target sand body, and use these sand bodies to deploy development horizontal wells.

[0113] In this embodiment of the invention, the relationship chart in step S02 is established using logging data, clay data, and water-sensitive data from the same core well and at the same depth.

[0114] In this embodiment of the invention, taking a certain work area of ​​an oilfield as an example, this oilfield work area has been under development for more than 60 years. Both the main and non-main oil layers are in the "high-temperature, high-volume, and high-efficiency" extraction stage, with decreasing potential for successor extraction and poor economic benefits. The upper water-sensitive reservoir is currently the only complete undeveloped oil layer in this oilfield, with great resource potential, and is the main target for the next stage of oilfield exploration and development. Due to the scattered planar distribution of the reservoir, its high water sensitivity, and the uneven distribution of water sensitivity, exploration and development are difficult. The method of this invention identifies high-quality target sand bodies with good oil content and low water sensitivity, and uses a three-dimensional model of the high-quality target sand bodies to deploy horizontal wells, achieving successor extraction in the oilfield. Specific steps include:

[0115] Step 1: Acquire data for the target work area, including seismic data, well logging data, clay data, and water-sensitive data. Seismic data includes processed post-stack seismic data volumes and seismic interpretation horizons; well logging data includes acoustic, density, gamma, spontaneous potential, and resistivity logging curves; clay data includes the absolute contents of montmorillonite, illite, kaolinite, chlorite, illite-montmorillonite mixed layers, chlorite-montmorillonite mixed layers, and total clay minerals in the target layer; water-sensitive data is the water sensitivity index of the target layer. Clay and water-sensitive data were obtained through laboratory experiments using actual formation cores.

[0116] Step 2: Compare the drilling depth of the standard layer of the core well in the work area with the electrical logging depth to find the difference between the two. Use the electrical logging depth as the standard to make corrections to obtain the accurate core depth and achieve core depth correction.

[0117] Step 3: The acoustic, density, gamma, and resistivity curves are preprocessed, including outlier handling and standardization; the spontaneous potential logging curve preprocessing includes outlier handling, baseline correction, and standardization; outlier handling involves replacing outliers with normal values ​​from the surrounding formation; standardization first involves batch standardization, followed by fine standardization; baseline correction first determines the positive or negative anomalies of the spontaneous potential curve, which is negative in this work area, and then calculates and corrects the baseline value.

[0118] Step 4: Comprehensively process and analyze clay data and water-sensitive data to establish a chart showing the relationship between the absolute contents of montmorillonite, illite, kaolinite, chlorite, illite-montmorillonite mixed layers, chlorite-montmorillonite mixed layers, and total clay minerals, and water-sensitive data. Analysis indicates that the absolute content of total clay minerals has the strongest correlation with the water-sensitive index. Figure 2 As shown, the absolute content of total clay minerals is used as a direct parameter for judging the degree of water sensitivity;

[0119] Step 5: Optimize the primary sensitive parameter. First, establish a chart showing the relationship between acoustic, density, gamma, spontaneous potential, resistivity logging data and the absolute content of total clay minerals. Analysis shows that gamma logging data has the best correlation with the absolute content of total clay minerals. Figure 3 As shown in the figure, combined with the relationship between the absolute content of total clay minerals and water-sensitive data in step four, gamma logging data can be used as an indirect parameter to determine the degree of water sensitivity. Therefore, gamma logging data is preferred as the first sensitive parameter.

[0120] Step Six: Optimize the Second Sensitive Parameter. Using gamma ray data as the X-axis, and one of the following logging data (sonic logging, density logging, spontaneous potential logging, or resistivity logging) as the Y-axis, with oil-bearing capacity as the data point color, create a graph showing the relationship between gamma ray, the second sensitive parameter, and oil-bearing capacity. Combining this with the first sensitive parameter, analysis suggests that resistivity logging data has the best correlation with oil-bearing capacity. Figure 4 As shown, resistivity logging data is preferred as the second sensitive parameter;

[0121] Step 7: Establish a simulated data volume for sensitive parameters by combining well and seismic logging. The first sensitive parameter, gamma ray logging data, is from the oilfield DLS logging series, with a small number of curves, making it suitable for establishing a gamma ray simulation data volume using seismic waveform difference simulation. The second sensitive parameter, resistivity logging data, has a large number of curves, making it more suitable for establishing a resistivity simulation data volume using seismic waveform indication simulation. The specific implementation process is as follows:

[0122] a. Using the aforementioned seismic data and well logging data, a detailed seismic synthetic record is produced to calibrate the accurate depth location and seismic reflection characteristics of each main target layer on the seismic profile, providing accurate time-depth relationships for the establishment of seismic waveform indication simulation and waveform difference simulation models;

[0123] b. Using the seismic interpretation horizons, select the top and bottom of the target layer and the major stratigraphic boundaries (group and segment interfaces) within the target layer to establish a target layer framework model for the target work area;

[0124] c. Correlation analysis was performed on the predicted curves estimated by different numbers of samples selected by the waveform indication method and the original curves to obtain the best-fit sample chart and determine the optimal number of samples. When the number of effective samples is larger, the threshold for screening similar seismic waveforms is lower. Conversely, when the number of effective samples is smaller, the threshold for screening similar seismic waveforms is higher. Finally, the optimal number of samples for the work area was determined to be 6 through chart analysis.

[0125] d. Calculate the optimal cutoff frequency of the model using the formula f_cutoff = v / 4d. The optimal cutoff frequency is the maximum frequency of the model. The smaller this parameter, the lower the resolution but the stronger the determinism; conversely, the higher the parameter, the higher the resolution but the stronger the randomness. Through calculation and analysis, the high-pass frequency of the model is found to be 380 Hz, and the high-cutoff frequency is 400 Hz.

[0126] e. Under the control of the frame model, seismic data volume, well logging data, seismic synthetic records, optimal sample size, and optimal cutoff frequency, the profiles of the gamma simulation data volume are obtained using model difference simulation and waveform indication simulation methods, respectively. Figure 5 As shown, and the cross-section of the resistivity simulation data volume, as... Figure 6 As shown.

[0127] Step 8: According to Figure 2 , Figure 3 Relationship analysis suggests that reservoirs with gamma values ​​less than 84 API are weakly or moderately water-sensitive reservoirs; according to Figure 4 Analysis of the relationship diagram suggests that reservoirs with resistivity greater than 10 Ω·m and gamma values ​​less than 90 API have good oil-bearing potential. Therefore, comprehensive analysis indicates that the range of sensitive parameters, gamma less than 84 API and resistivity greater than 10 Ω·m, represents the target sand body range.

[0128] By combining the target sand body extent, gamma simulation data volume, and resistivity data volume, a three-dimensional model of the target sand body is established, such as... Figure 7 As shown.

[0129] Step Nine: Extract the well placement sand bodies from the 3D model of the target sand body, such as... Figure 8 The image shows a seismic waveform overlay of the target sand body profile. A development horizontal well is deployed using this sand body, as shown below. Figure 9The image shown is a drilling effect diagram of a small layer of the target sand body.

[0130] In actual implementation, three horizontal wells in the target area have been completed, with horizontal section lengths of 316m, 335m, and 433m respectively. The oil layer encounter rate is 96.3%, and the water sensitivity is moderate. These results demonstrate that the method of this invention is effective in finding high-quality reservoirs with good oil content and relatively low water sensitivity, providing strong support for stable oilfield production.

[0131] It is understood that the various method embodiments mentioned above in this invention can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this invention will not elaborate further.

[0132] The execution entity of the target sand body identification method based on water-sensitive reservoir parameters can be a target sand body identification device based on water-sensitive reservoir parameters. For example, the method can be executed by a terminal device, server, or other processing device. The terminal device can be a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, vehicle-mounted device, wearable device, etc. In some possible implementations, the target sand body identification method based on water-sensitive reservoir parameters can be implemented by a processor calling computer-readable instructions stored in memory.

[0133] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0134] This invention also provides a target sand body identification device based on water-sensitive reservoir sensitive parameters, comprising: an acquisition unit for acquiring seismic data, well logging data, clay data, and water-sensitive data of the work area; a relationship chart establishment unit for establishing relationship charts between clay data, well logging data, and water-sensitive data, and determining, based on the relationship charts, the optimal clay mineral in the clay data that has the best correlation with the water-sensitive data, and determining, based on the well logging data, the sensitive parameter that has the best correlation with the optimal clay mineral and oil-bearing properties; a simulation data volume establishment unit for establishing a simulation data volume of the sensitive parameter based on the seismic data, using seismic waveform difference simulation or seismic waveform indication simulation; a target sand body range determination unit for determining the range of the target sand body based on the relationship charts; and a model establishment unit for establishing a three-dimensional model of the target sand body based on the target sand body range and the simulation data volume of the sensitive parameter, thereby completing the target sand body identification.

[0135] In some embodiments, the functions or modules and units included in the apparatus provided by the present invention can be used to execute the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0136] This invention also proposes a computer-readable storage medium storing computer program instructions thereon, which, when executed by a processor, implement the above-described method. The computer-readable storage medium may be a non-volatile computer-readable storage medium.

[0137] This invention also provides an electronic device, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured as described above. The electronic device can be provided as a terminal, a server, or other type of device.

[0138] This invention fully utilizes the advantages of high accuracy of core data, high vertical resolution of well logging data, and dense lateral sampling of seismic data. By combining rock logging and well logging, the sensitive parameters and constraint range of the target sand body are clearly identified. By combining well logging and seismic data, a three-dimensional model of the target sand body is established. This allows for the identification of high-quality target sand bodies with good oil content and relatively low water sensitivity. Horizontal wells can be deployed using the three-dimensional model of the high-quality target sand body to achieve oilfield succession production and provide strong support for stable oilfield production.

[0139] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for identifying target sand bodies based on water-sensitive reservoir sensitivity parameters, characterized in that, The method comprises the following steps: acquiring seismic data, logging data, clay data and water sensitivity data of a work area; establishing a relationship chart between the clay data, the logging data and the water sensitivity data respectively, determining the best clay mineral in the clay data which has the best correlation with the water sensitivity data according to the relationship chart, and determining the sensitive parameter in the logging data which has the best correlation with the best clay mineral and oiliness; establishing a simulation data volume of the sensitive parameter by using seismic waveform difference simulation or seismic waveform indication simulation according to the seismic data; determining the range of a target sand body according to the relationship chart; establishing a three-dimensional model of the target sand body according to the range of the target sand body and the simulation data volume of the sensitive parameter, and completing the identification of the target sand body.

2. The method according to claim 1, wherein the seismic data at least comprises a post-stack seismic data volume and a seismic interpreted horizon.

3. The method according to claim 1, wherein the logging data at least comprises acoustic wave, density, gamma, natural potential and resistivity logging curves.

4. The method according to claim 1, wherein the clay data at least comprises the absolute content of montmorillonite, illite, kaolinite, chlorite, illite-smectite mixed layer, chlorite-smectite mixed layer and total clay mineral in the target layer. Before the relationship chart between the clay data, the logging data and the water sensitivity data is established, the core depth is corrected by the following method: comparing the drill tool depth and the electrical measuring depth of the standard layer of the coring well to determine the difference therebetween, correcting the drill tool depth according to the electrical measuring depth to obtain the corrected core depth. Before the relationship chart between the clay data, the logging data and the water sensitivity data is established, the logging data is preprocessed by the following method:

5. The method for identifying target sand bodies based on water-sensitive reservoir sensitivity parameters according to claim 1, wherein, the acoustic wave, density, gamma, resistivity logging curves are subjected to abnormal value processing and standardization; the natural potential logging curve is subjected to abnormal value processing, baseline correction and standardization.

6. The method for identifying target sand bodies based on water-sensitive reservoir sensitivity parameters according to claim 3, characterized in that, The method for establishing the relationship chart between the clay data, the logging data and the water sensitivity data respectively, and determining the best clay mineral in the clay data which has the best correlation with the water sensitivity data according to the relationship chart, comprises the following steps: relationship charts between the absolute content of each clay mineral and the total clay mineral in the clay data and the water sensitivity data are established respectively, and the clay mineral which has the best correlation with the water sensitivity data is determined as the best clay mineral. The method for establishing the relationship chart between the clay data, the logging data and the water sensitivity data respectively, and determining the sensitive parameter in the logging data which has the best correlation with the best clay mineral and oiliness according to the relationship chart, comprises the following steps:

7. The method for identifying target sand bodies based on water-sensitive reservoir sensitivity parameters according to claim 1, wherein, the sensitive parameter comprises a first sensitive parameter and a second sensitive parameter; relationship charts between each logging curve in the logging data and the absolute content of the best clay mineral are established respectively, and the logging curve which has the best correlation with the best clay mineral is determined as the first sensitive parameter; 8. The method for identifying target sand bodies based on water-sensitive reservoir sensitivity parameters according to claim 1, wherein, ​ ​ ​ A relationship chart between each logging curve and the first sensitive parameter and oil-bearing property in the logging data is established respectively, and a logging curve with the best correlation with the first sensitive parameter and oil-bearing property is determined as the second sensitive parameter.

9. The method for identifying target sand bodies based on water-sensitive reservoir sensitivity parameters according to claim 8, characterized in that, The method for determining the target sand body range according to the relationship chart comprises: selecting a target water sensitivity and oil-bearing property; determining a target sand body range that meets the target water sensitivity and oil-bearing property requirements on the relationship chart according to a water sensitivity data value range corresponding to the target water sensitivity.

10. The method for identifying target sand bodies based on water-sensitive reservoir sensitivity parameters according to claim 9, wherein, The water sensitivity data value range corresponding to the water sensitivity comprises: The water sensitivity comprises: no water sensitivity, weak water sensitivity, medium-weak water sensitivity, medium-strong water sensitivity, strong water sensitivity, and extremely strong water sensitivity; The water sensitivity data Wsd value range corresponding to the water sensitivity is: when Wsd≤0.05, it is no water sensitivity; when 0.05 11. The method for identifying target sand bodies based on water-sensitive reservoir sensitivity parameters according to claim 1, wherein, The method for establishing the simulation data volume of the sensitive parameter according to the seismic data by using seismic waveform difference simulation or seismic waveform indication simulation comprises: producing a seismic synthetic record by using the seismic data and the logging data; selecting a target layer top and bottom and a target layer internal group and section stratum interface according to the seismic data to establish a target layer framework model of the working area; determining the best sample number by using the waveform indication method; determining the best cutoff frequency value of the model according to a cutoff frequency calculation formula; obtaining the simulation data volume of the sensitive parameter by using seismic waveform difference simulation or seismic waveform indication simulation according to the framework model, the seismic data, the logging data, the seismic synthetic record, the best sample number, and the best cutoff frequency.

12. The method for identifying target sand bodies based on water-sensitive reservoir sensitivity parameters according to claim 11, wherein, The method for determining the best sample number by using the waveform indication method comprises: optimizing different numbers of samples by using the waveform indication method; estimating corresponding prediction curves according to different numbers of samples; performing correlation analysis on the prediction curves and the original curves to obtain a fitting best sample chart; when the correlation coefficient in the fitting best sample chart no longer changes or the change amplitude is below a predetermined amplitude, the sample number corresponding to the correlation coefficient is the best sample number.

13. The method for identifying target sand bodies based on water-sensitive reservoir sensitivity parameters according to claim 1, wherein, Further comprising: extracting a target well deployment horizon sand body in the target sand body three-dimensional model, and deploying a development horizontal well by using the target well deployment horizon sand body.

14. A target sand body identification device based on water-sensitive reservoir sensitive parameters, characterized in that, Comprise: an acquisition unit configured to acquire seismic data, logging data, clay data, and water sensitivity data of a working area; a relationship chart establishment unit configured to establish relationship charts among the clay data, the logging data, and the water sensitivity data respectively, and determine a best clay mineral with the best correlation with the water sensitivity data in the clay data and a sensitive parameter with the best correlation with the best clay mineral and oil-bearing property in the logging data according to the relationship charts; a simulation data volume establishment unit configured to establish a simulation data volume of the sensitive parameter by using seismic waveform difference simulation or seismic waveform indication simulation according to the seismic data; and A target sand body range determining unit is configured to determine a target sand body range according to the relationship chart; A model establishing unit is configured to establish a three-dimensional model of the target sand body according to the target sand body range and the simulation data body of the sensitive parameter, and complete the target sand body identification.

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