Method for predicting productivity of complex lithologic oil and gas reservoir, storage medium and device

Abstract

The invention relates to a method, a storage medium and a device for predicting the productivity of a complex lithologic oil and gas reservoir, and the method comprises the following steps: quantitatively calculating the proportions of various lithologic components in a complex lithologic sand layer, and outputting the profile data of the complex lithologic sand layer; quantitatively calculating the proportions of various fluids of the complex lithologic oil and gas reservoir, and outputting pore fluid distribution profile data; on the basis of carrying out core homing and overburden pressure correction on the core porosity and permeability data, respectively establishing correlativity between an acoustic curve and a porosity curve and between a permeability curve and a porosity curve for different lithologic components, and outputting a porosity and permeability calculation model; the fluid scale is calculated through a main variable and branch variable multi-parameter integrated analogy algorithm by adopting the principle of heteroassimilation and homodissimilation of an analogy method; and outputting productivity prediction based on the fluid scale of qualitative judgment and quantitative calculation of fluid properties. As a new method for predicting the productivity of the complex lithologic oil and gas reservoir, the method can be beneficial to accurate evaluation decision and efficient construction and production of the complex lithologic oil and gas reservoir.

Description

Technical Field

[0001] The present invention relates to the field of reservoir productivity prediction, and particularly to a method, a storage medium, and a device for predicting the productivity of complex lithology oil and gas reservoirs. Background Art

[0002] With the continuous deepening of exploration and development work, the production decline rate of carbonate rock reservoirs in oilfields is relatively fast, which will lead to a continuous increase in shut-in wells and low-production and inefficient wells. At this time, the re-inspection of old clastic rock wells in the overlying strata highlights an important role. Finding an oil and gas layer in an old well can save a shut-in well. In the case of the urgent need for re-inspection of old clastic rock wells in oil production plants, the fine logging evaluation technology of oil and gas reservoirs is particularly important. For example, the Kalashayi Formation of the Carboniferous System, as one of the main producing layers of clastic rocks in the Tahe Oilfield, shoulders an important task of increasing reserves and production. However, due to the complex lithology, rapid lateral variation of sand bodies, and strong heterogeneity of the Kalashayi Formation of the Carboniferous System, the previous understanding of the Carboniferous System was insufficient, resulting in a generally low coincidence rate of logging interpretation of the Carboniferous System. Wells with overestimated interpretations will render the upward return measures ineffective and increase the cost of upward return measures for old wells. Wells with underestimated interpretations or uninterpreted wells also reduce the effect of increasing reserves and production. The productivity prediction technology for complex lithology oil and gas reservoirs in the Carboniferous System was basically blank in the early stage, and the effect of perforation measures for upward return in the Carboniferous System could not be predicted, resulting in a large amount of economic losses.

[0003] For the Chinese patent "A method for predicting the annual productivity after hydraulic fracturing of horizontal wells" with the patent number CN202111275088.4, the patented technology is to select a horizontal well, determine the single-well productivity quality and sensitive fracturing parameters of the annual productivity of the horizontal well, and use the single-well productivity quality and sensitive fracturing parameters of the selected horizontal well to construct a prediction model for the annual productivity of the horizontal well; according to the prediction model of the annual productivity of the horizontal well, predict the annual productivity of the horizontal well to be predicted.

[0004] For the Chinese patent "A method for predicting the productivity of fractured oil and gas reservoirs" with the patent number CN202310288638.9, the patented technology is to invert the natural fracture morphology through microseismic monitoring and FMI logging imaging data, and add the obtained natural fracture parameters to the fracture propagation and productivity prediction process to realize the productivity calculation of fractured reservoirs.

[0005] The productivity prediction methods disclosed in the above patents cannot be applied to the calculation of complex lithology sand layers and cannot accurately predict the productivity of complex lithology oil and gas reservoirs. Therefore, there is an urgent need to develop a new method for predicting the productivity of complex lithology oil and gas reservoirs to support the accurate evaluation decision-making and efficient production construction of complex lithology oil and gas reservoirs. Summary of the Invention

[0006] The present invention solves the problem that the current production capacity prediction method cannot be applied to the calculation of complex lithology sand layers, thus unable to accurately predict the production capacity of complex lithology sand layers. It provides a method, storage medium and device for predicting the production capacity of complex lithology oil and gas reservoirs to solve this technical problem, identify the specific lithology of complex lithology sand layers and calculate the lithology profile, identify the fluid properties of complex lithology sand layers and calculate the pore fluid distribution profile, then establish a porosity and permeability calculation model and calculate the pore and permeability parameters, and calculate the fluid scale of complex lithology oil and gas reservoirs through the production capacity prediction model, so as to output the production capacity prediction of complex lithology oil and gas reservoirs, thereby supporting the accurate evaluation decision-making and efficient production construction of complex lithology oil and gas reservoirs.

[0007] To solve the above technical problems, the technical solution of the present invention is as follows:

[0008] A method for predicting the production capacity of complex lithology oil and gas reservoirs, comprising the following steps:

[0009] S1. Discriminate the specific lithology of the complex lithology sand layer, identify the specific lithology of the complex lithology sand layer, and quantitatively calculate the proportion of various lithology components, and output the profile data of the complex lithology sand layer;

[0010] S2. Respectively establish a logging interpretation chart and logging interpretation standard for complex lithology oil and gas reservoirs for different lithology components, identify the fluid properties of complex lithology oil and gas reservoirs, and quantitatively calculate the proportion of various fluids, and output the pore fluid distribution profile data;

[0011] S3. On the basis of core hole-permeability data core positioning and overburden pressure correction, respectively establish the correlation between the acoustic wave curve and the porosity curve for different lithology components, establish the correlation between the porosity curve and the permeability curve, and output the porosity and permeability calculation model of the complex lithology sand layer;

[0012] S4. Using the principle of heterogeneous assimilation and homogeneous differentiation of the analogy method, analyze the heterogeneous assimilation of the main variables and sub-variables of the high-yield oil and gas layer parameter changes, and differentiate the presence and magnitude of variables such as porosity, permeability, resistivity, reservoir thickness, maximum total hydrocarbon, and maximum C1. Calculate the fluid scale of complex lithology oil and gas reservoirs through the main variable and sub-variable multi-parameter integrated analogy algorithm;

[0013] S5. Based on the qualitative discrimination of the fluid properties of the complex lithology oil and gas reservoir and the quantitatively calculated fluid scale, output the production capacity prediction of the complex lithology oil and gas reservoir.

[0014] Preferably, in step S1, the method for discriminating the specific lithology of the complex lithology sand layer includes:

[0015] The conventional curve qualitative identification method is used to identify lithology based on the different response characteristics of conventional curves on different lithologies;

[0016] The imaging logging qualitative identification method is used to identify lithology based on the different display characteristics of imaging logging on different lithologies;

[0017] The lithology chart quantitative identification method is used to identify lithology based on the different areas of the lithology chart where the characteristic points of different lithologies fall.

[0018] Preferably, in step S1, the method for outputting complex lithology profile data is as follows:

[0019] First, qualitatively determine which lithology components are present in the complex lithology sand layer;

[0020] Then, according to the different lithology components, select different processing parameters in the THsand processing program to process the logging curves, and the processed results are displayed using different lithology profiles according to the specific lithology of the complex lithology sand layer.

[0021] Preferably, in step S2, the methods for identifying the fluid properties of complex lithology hydrocarbon reservoirs include:

[0022] The complex lithology fluid property qualitative identification method, which is used to identify based on the resistivity, total hydrocarbon value, total hydrocarbon curve morphology, single peak of the nuclear magnetic T2 spectrum, and differential spectrum characteristics of oil and gas layers, oil-water coexisting layers, oil-bearing water layers, and water layers;

[0023] The complex lithology fluid property quantitative identification method: Sort out the data of all current test wells, conduct logging and gas logging analysis on the complex lithology test wells, select the most representative depth points, re-read all test layers, and draw logging interpretation charts and gas logging interpretation charts; then establish fluid identification charts for different lithology components with different petrophysical parameters respectively.

[0024] Preferably, in step S3, in order to accurately calculate the porosity and permeability parameters, it is necessary to establish a porosity and permeability evaluation model by region and lithology.

[0025] Preferably, in step S4, using the analog assimilation of the main variable C1 and the inter-class discriminability of the sub-variables C2, C3, iC4, nC4, iC5, nC5, carry out a multi-parameter integrated analog algorithm, calculate and fit the functional changes of the main variable and the aggregate index of different sub-variables at multiple levels, and construct the Koc and Kw fluid identification indicators and separation characteristic spaces:

[0026]

[0027] Kw = C1 / (C2 + C3 + iC4 + nC4 + iC5 + nC5)

[0028] In the formula, C1, C2, C3, iC4, nC4, iC5, and nC5 are the data recorded during logging of methane, ethane, propane, isobutane, n-butane, isopentane, and n-pentane in gas logging data. Koc: oil and gas abundance index, Kw: represents the dry-wet index;

[0029] Construct a two-dimensional slope space model with Koc as the vertical coordinate and Kw as the horizontal coordinate, and use the spatial distribution law of Koc-Kw indexes of typical oil layers, gas layers, water layers, and oil-water layers to depict the spatial separation model of the oil-water layer area, oil area, gas area, and water area of the three-phase fluid.

[0030] Preferably, when predicting the productivity of complex lithology oil and gas reservoirs, obtain porosity, permeability, resistivity, reservoir thickness, maximum total hydrocarbon, maximum C1, and empirical coefficient m from actual logging and mud logging data. The productivity prediction formula for the complex lithology oil and gas reservoir can be approximately expressed as:

[0031] q = m·f(h, φ, k, R, Koc, Kw)

[0032] In the formula, q is the predicted productivity, m is the empirical coefficient, h is the effective thickness of the reservoir, φ is the effective porosity of the reservoir, k is the effective permeability of the reservoir, R is the resistivity of the reservoir, Koc is the oil and gas abundance index, and Kw is the dry-wet index.

[0033] Preferably, the empirical coefficient m needs to be set according to the test productivity of the oil and gas well, fit the relationship between the production of the oil and gas well and the logging parameters, and determine the empirical coefficient m according to the obtained fitting formula.

[0034] A computer-readable storage medium stores computer-executable instructions therein, and when the computer-executable instructions are executed by a processor, they are used to implement the above method for predicting the productivity of complex lithology oil and gas reservoirs.

[0035] A computer device includes a processor and a memory. The memory stores a computer program, and the computer program is loaded and executed by the processor to implement the above method for predicting the productivity of complex lithology oil and gas reservoirs.

[0036] The beneficial technical effects of the technical solution of the present invention:

[0037] (1) In the solution of the present invention, by identifying the specific lithology of complex lithologic sand layers, calculating the lithologic profile, identifying the fluid properties of complex lithologic sand layers, and calculating the pore fluid distribution profile, a porosity and permeability calculation model is established, and the pore and permeability parameters are calculated. Finally, through the production capacity prediction model, the fluid scale of complex lithologic oil and gas reservoirs is calculated, and the purpose of outputting the production capacity prediction of complex lithologic oil and gas reservoirs is achieved. A corresponding relationship is established between the logging curves and the oil and gas production capacity of complex lithologic oil and gas reservoirs, and a new application method for logging curves is developed, which can realize the production capacity prediction of complex lithologic oil and gas reservoirs and strongly support the production increase work.

[0038] (2) In the quantitative identification method of complex lithologic fluid properties, logging and gas logging analysis are carried out on the test wells, the test layers are re-read, and the logging and gas logging interpretation charts are drawn. The interpretation charts of acoustic wave and resistivity and the total hydrocarbon C1 chart have good effects (reflecting the porosity more accurately), and can effectively distinguish oil and gas layers from water dry layers. However, there is a transition zone between the logging interpretation chart and the gas logging interpretation chart, and the fluid properties in the transition zone are relatively mixed, making it difficult to accurately judge. At this time, fluid identification charts are established using different rock-electric parameters for the components of different lithologies respectively. After establishing the charts by lithology, the problem of the transition zone in the charts is effectively solved, and the logging interpretation coincidence rate of complex lithologic sand layers can be greatly improved. Description of the Drawings

[0039] Figure 1 Shows the flow chart of the method for predicting the production capacity of complex lithologic oil and gas reservoirs in the embodiment of the present invention;

[0040] Figure 2 Shows the intuitive diagram of the pore fluid distribution obtained in step S2 in the embodiment of the present invention;

[0041] Figure 3 Shows the schematic diagram of the correlation between the acoustic wave curve and the porosity curve in the embodiment of the present invention;

[0042] Figure 4 Shows the schematic diagram of the correlation between the porosity curve and the permeability curve in the embodiment of the present invention. Detailed Embodiment

[0043] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the following further elaborates in detail a method, storage medium and device for predicting the productivity of complex lithologic oil and gas reservoirs proposed by the present invention in combination with the accompanying drawings and specific embodiments. According to the following description, the advantages and features of the present invention will be clearer. It should be noted that the accompanying drawings are in a very simplified form and all use non-precise scales, only for conveniently and clearly assisting in explaining the objectives of the embodiments of the present invention. In order to make the objectives, features and advantages of the present invention more obvious and understandable, please refer to the accompanying drawings. It should be noted that the structures, ratios, sizes, etc. shown in the drawings of this specification are only used to cooperate with the content disclosed in the specification for those skilled in this technology to understand and read, and are not used to limit the limiting conditions for the implementation of the present invention. Therefore, they do not have technical substance. Any modification of the structure, change of the proportional relationship or adjustment of the size, without affecting the effects that the present invention can produce and the objectives that can be achieved, should still fall within the scope covered by the technical content disclosed by the present invention.

[0044] The following will elaborate in detail the technical solutions of a method, storage medium and device for predicting the productivity of complex lithologic oil and gas reservoirs of the present invention in combination with the Figures 1 to 4 accompanying drawings and specific embodiments.

[0045] Embodiment

[0046] As Figures 1 to 4 shown, a method for predicting the productivity of complex lithologic oil and gas reservoirs in this embodiment includes the following steps:

[0047] S1. Discriminate the specific lithology of the complex lithologic sand layer, identify the specific lithology of the complex lithologic sand layer, and quantitatively calculate the proportion of various lithologic components, and output the profile data of the complex lithologic sand layer;

[0048] S2. Establish logging interpretation charts and logging interpretation standards for complex lithologic oil and gas reservoirs for different lithologic components respectively, identify the fluid properties of the complex lithologic oil and gas reservoirs, and quantitatively calculate the proportion of various fluids, and output the pore fluid distribution profile data;

[0049] S3. On the basis of core location and overburden pressure correction for core porosity and permeability data, establish the correlation between the acoustic wave curve and the porosity curve for different lithologic components respectively, establish the correlation between the porosity curve and the permeability curve, and output the porosity and permeability calculation models of the complex lithologic sand layer;

[0050] S4. Using the principles of heterogeneous assimilation and homogeneous dissimilation in the analogy method, analyze the heterogeneous assimilation of the main variables and sub-variables of the parameter changes in high-yield oil and gas reservoirs. The differences in differentiation are the presence and magnitude of variables such as porosity, permeability, resistivity, reservoir thickness, maximum total hydrocarbon, and maximum C1. Calculate the fluid scale of complex lithology oil and gas reservoirs through the multi-parameter integration analogy algorithm of the main variable and sub-variables.

[0051] S5. Based on the qualitative discrimination of the fluid properties of the complex lithology oil and gas reservoirs and the quantitatively calculated fluid scale, output the productivity prediction of the complex lithology oil and gas reservoirs.

[0052] When using the method in this embodiment for identification, the complex lithology sand layer is the formation where the complex lithology oil and gas reservoir is located. The methods for discriminating the specific lithology of the complex lithology sand layer include:

[0053] 1) The conventional curve qualitative identification method is a method for lithology identification based on the different response characteristics of conventional curves on different lithologies. Through comparative analysis, the gamma and density curves are sensitive to the grain size of sandstone, and these two curves are mainly used to distinguish the grain size. The specific characteristics of different lithologies are as follows:

[0054] Logging curve characteristics of pure sandstone with different grain sizes: For coarse sandstone, the gamma is about 45 API and the density is about 2.4 g / cm; for medium sandstone, the gamma is about 53 API and the density is about 2.45 g / cm; for fine sandstone, the gamma is about 60 API and the density is about 2.55 g / cm; for siltstone, the gamma is about 75 API and the density is about 2.6 g / cm.

[0055] Logging curve characteristics of gravelly coarse sandstone (gravel content < 10%): The logging curve characteristics of gravelly coarse sandstone are relatively close to those of coarse sandstone, and the physical properties are good.

[0056] Logging curve characteristics of Carboniferous gravelly coarse sandstone (10% < gravel content < 30%): The logging curve characteristics of gravelly coarse sandstone are relatively complex. The logging characteristics of gravelly coarse sandstone with a relatively small gravel diameter are close to those of coarse sandstone, and the logging characteristics of gravelly coarse sandstone with a relatively large gravel diameter are close to those of sandy conglomerate. Different gravel properties also have a greater impact on logging characteristics. Quartz gravel, calcareous gravel, siliceous gravel, and chert gravel will reduce the gamma, while mud gravel will increase the gamma.

[0057] Logging curve characteristics of Carboniferous sandy conglomerate (gravel content > 30%): The gamma is below 35 API, the resistivity is above 10 Ω·m, and the density is above 2.5 g / cm3. The higher the gravel content, the lower the gamma, and the larger the gravel diameter of the gravel, the lower the gamma. The gamma of calcareous sandstone generally will not be lower than 30 API, and those with a gamma lower than 30 API are all gravelly sandstone or sandy conglomerate.

[0058] Logging curve characteristics of Carboniferous ash-bearing sandstone (ash content < 15%): The logging curve characteristics of ash-bearing sandstone are relatively close to those of pure sandstone, and the logging curve characteristics mainly depend on the particle size.

[0059] Logging curve characteristics of Carboniferous argillaceous sandstone (15% < ash content < 30%): The gamma of argillaceous sandstone is lower than that of pure sandstone. The logging curve characteristics of argillaceous sandstone with different particle sizes are different. With a relatively high ash content above 15%, it has a greater impact on the logging curve, resulting in a decrease in gamma and a deterioration of porosity.

[0060] 2) The imaging logging qualitative identification method is a method for lithology identification based on the different display characteristics of imaging logging for different lithologies. The specific characteristics of different lithologies are as follows:

[0061] Imaging characteristics of sandstone with different particle sizes: Coarse sandstone shows a relatively rough image feature, while fine sandstone shows a relatively clear image feature. The finer the particle size, the clearer the image. If there are gravels in the sandstone, it will show a bright spot-like feature.

[0062] Imaging characteristics of conglomerate: Conglomerate shows a dense bright patch-like feature in imaging, which is relatively easy to identify. Imaging can reflect information such as the size, density, sorting, and rounding of gravels.

[0063] Imaging characteristics of mudstone: Mudstone usually shows a blurred and poor bedding feature due to hole enlargement. Generally, sandstone has better imaging quality and bedding.

[0064] 3) The lithology chart quantitative identification method is a method for lithology identification based on the different areas of the lithology chart where the characteristic points of different lithologies fall. The specific characteristics of different lithologies are as follows:

[0065] In this embodiment, crossplots are drawn based on the core calibration results of 56 wells. By comparing several crossplots, the GR-DEN crossplot has the best lithology identification effect and can effectively distinguish sandstones with different particle sizes, and can be used as the lithology identification chart for the Carboniferous. The abscissa is GR (natural gamma), and the ordinate is DEN (density). It is classified according to the sandstone types with different particle sizes into 5 types: conglomerate, coarse sandstone, medium sandstone, fine sandstone, and siltstone. The distribution areas of these 5 types on the crossplot are different, and lithology identification is carried out based on this. The curves in the Carboniferous that are sensitive to lithology are the gamma curve and the density curve. Based on the values of these two curves, the particle size of the sandstone can be analyzed. Generally, the finer the particle size, the higher the gamma and the greater the density.

[0066] Method for outputting complex lithology profile: According to the three identification methods for complex lithology, first qualitatively determine which lithology components are present in the rock. Based on the different lithologies, different processing parameters are selected in the THsand processing program to process the logging curves. Then, according to the specific lithologies determined by the above three identification methods, different lithology profiles are selected for display.

[0067] Specifically, the methods for identifying the fluid properties of complex lithology hydrocarbon reservoirs in step S2 include:

[0068] Qualitative identification method for complex lithology fluid properties: Identify based on the resistivity, total hydrocarbon value, total hydrocarbon curve morphology, single peak of nuclear magnetic T2 spectrum, and differential spectrum characteristics of oil and gas layers, oil-water layers, water-bearing oil layers, and water layers.

[0069] Quantitative identification method for complex lithology fluid properties: Sort out the data of all current test wells, conduct logging and gas logging analysis on complex lithology test wells, select the most representative depth points, re-read the values of all test layers, and draw logging interpretation charts and gas logging interpretation charts; then establish fluid identification charts for different lithology components with different rock-electric parameters respectively.

[0070] In the actual operation process, when qualitatively identifying the fluid properties of complex lithology, in this embodiment, taking the complex lithology hydrocarbon reservoir in the Carboniferous system of Tahe as an example, some actual data are as follows:

[0071] For oil and gas layers, the resistivity is above 3 Ω·m, the total hydrocarbon value is above 4%, the total hydrocarbon curve shows a high-value peak-like characteristic, the single peak of the nuclear magnetic T2 spectrum is behind, the distribution is wide and the shape is flat, and the differential spectrum signal is strong and behind.

[0072] For oil-water layers, the resistivity is 2 - 3 Ω·m, the total hydrocarbon is 1 - 4%, the total hydrocarbon curve shows a low-value gentle slope-like characteristic, the single peak of the nuclear magnetic T2 spectrum is behind, the distribution is wide and the shape is flat, and the differential spectrum signal is strong and in the middle.

[0073] For water-bearing oil layers, the resistivity is 1.5 - 2 Ω·m, the total hydrocarbon is 0.7 - 1%, the total hydrocarbon curve shows a low-value gentle slope-like characteristic, the nuclear magnetic T2 spectrum has a double peak in the middle, the distribution is narrow and the shape is steep, and the differential spectrum signal is weak and in the middle.

[0074] For water layers, the resistivity is below 1.5 Ω·m, the total hydrocarbon is below 0.7%, the total hydrocarbon curve shows a low-value gentle slope-like characteristic, the nuclear magnetic T2 spectrum has a double peak in the middle, the distribution is narrow and the shape is steep, and the differential spectrum signal is weak and in the middle.

[0075] When quantitatively identifying the fluid properties of complex lithologies, the data of all current test wells were systematically sorted out. Logging and gas logging analyses were carried out on the Carboniferous test wells. The most representative depth points were selected, and all test layers were reread. In this embodiment, 263 test layers of 152 wells were selected to draw interpretation charts. The interpretation charts of acoustic wave and resistivity and the total hydrocarbon C1 chart have good effects (reflecting porosity more accurately), and can effectively distinguish oil and gas layers from water layers and dry layers. However, there is a transition zone between the logging interpretation chart and the gas logging interpretation chart. The fluid properties in the transition zone are relatively mixed and it is difficult to accurately judge. After statistically analyzing the petrophysical parameters of the Carboniferous of 10 test wells, three lithological components of pure sandstone, gravelly sandstone, and calcareous sandstone were distinguished, and fluid identification charts were established with different petrophysical parameters. After establishing the charts by lithology, the problem of the transition zone in the charts was effectively solved, and the coincidence rate of Carboniferous logging interpretation was greatly improved.

[0076] The pore fluid distribution profile calculated in step S2 is a vertical profile, similar to the logging chart, which is calculated based on the logging curves and corresponds to the depth of the logging curves. For example, Figure 2 is an intuitive diagram of the fluid distribution in the pores. The left outer line POR in the figure is the porosity, the right outer line PORW is the water volume, and the middle curve PORF is the water volume in the flushed zone. The part of POR - PORF represents the residual oil volume, the part of PORF - PORW represents the movable oil volume, and the part of PORW - the right outer line represents the water volume.

[0077] Specifically, in step S3, based on the core displacement and overburden pressure correction of the core porosity and permeability data, the correlation between the acoustic wave curve and the porosity curve is established for different lithological components respectively, the correlation between the porosity curve and the permeability curve is established, and a calculation model for complex lithology porosity and permeability is output.

[0078] When actually applying this method, by comparing the correlation between the core porosity and the acoustic wave, neutron, and density porosities of 49 wells, it is found that the correlation between the acoustic wave porosity and the core porosity is the best. Since the density curve is greatly affected by lithology and a part of the curve reflects lithology information, the calculated porosity is not as accurate as the acoustic wave. Therefore, the acoustic wave curve is selected to establish the porosity evaluation model. The physical properties of different lithologies in different blocks of Tahe are quite different. In order to accurately calculate the porosity and permeability parameters, it is necessary to establish porosity and permeability evaluation models by block and by lithology. Based on the core displacement and overburden pressure correction of the core porosity and permeability data, establishing the porosity and permeability evaluation models of the Carboniferous by block and by lithology can improve the calculation accuracy of the porosity and permeability parameters.

[0079] In step S4, by applying the principles of heterogeneous assimilation and homogeneous differentiation of the analogy method, the heterogeneous assimilation of the main variables and sub-variables of the parameter changes in high-yield oil and gas reservoirs is analyzed. The differences in differentiation lie in the presence and magnitude of variables such as porosity, permeability, resistivity, reservoir thickness, maximum total hydrocarbon value, and maximum C1 value. The fluid scale of complex lithologic oil and gas reservoirs is calculated through a multi-parameter integrated analogy algorithm of the main variables and sub-variables.

[0080] In practical applications, analyze the heterogeneous assimilation of the main variables and sub-variables of the gas logging data of typical gas layers, oil layers, oil-water coexisting layers, and water layers in the Karamay Formation in different blocks. Utilize the analogy assimilation of the main variable C1 and the inter-class discriminability of the sub-variables C2, C3, iC4, nC4, iC5, and nC5 to carry out a multi-parameter integrated analogy algorithm, and calculate and fit the functional changes of the aggregate index of the main variable and different sub-variables at multiple levels to construct Koc and Kw fluid identification indicators and separation characteristic spaces:

[0081]

[0082] Kw = C1 / (C2 + C3 + iC4 + nC4 + iC5 + nC5)

[0083] In the formula, C1, C2, C3, iC4, nC4, iC5, and nC5 are the data recorded during logging of methane, ethane, propane, isobutane, n-butane, isopentane, and n-pentane in the gas logging data. Koc: oil and gas abundance index, Kw: represents the wet-dry index, which affects the fluidity and is related to whether the reservoir can produce and the amount of production;

[0084] Construct a two-dimensional slope space model with Koc as the vertical coordinate and Kw as the horizontal coordinate, and use the spatial distribution law of the Koc-Kw indicators of typical oil layers, gas layers, water layers, and oil-water coexisting layers to depict the spatial separation model of the oil-water coexisting layer area, oil area, gas area, and water area of the three-phase fluid.

[0085] When predicting the productivity of complex lithologic oil and gas reservoirs, combine Darcy's formula to obtain porosity, permeability, resistivity, reservoir thickness, maximum total hydrocarbon value, maximum C1 value from actual logging and mud logging data, and obtain the empirical coefficient m from the actual measured data. The productivity prediction formula for complex lithologic oil and gas reservoirs can be approximately expressed as:

[0086] q = m·f(h, φ, k, R, Koc, Kw)

[0087] In the formula, q is the predicted productivity, m is the empirical coefficient, h is the effective thickness of the reservoir, φ is the effective porosity of the reservoir, k is the effective permeability of the reservoir, R is the resistivity of the reservoir, Koc is the oil and gas abundance index, and Kw is the wet-dry index.

[0088] Among them, predicting the production capacity q requires using one or more parameters such as the effective porosity (φ) of the reservoir, the function of the effective thickness (h) of the reservoir, the effective permeability (k) of the reservoir, the resistivity (R) of the reservoir, the hydrocarbon abundance index (Koc), and the wet-dry index (Kw). To determine the empirical coefficient m in the formula, it is necessary to establish the relationship between the production per meter of the oil and gas well and the logging parameters based on the measured production capacity of the oil and gas well, and then determine the empirical coefficient m based on the fitting formula of the production of the oil and gas well and the logging parameters.

[0089] Figure 2 The figure shows a schematic diagram of the correlation between the acoustic wave curve and the porosity curve measured by the method according to this solution. By comparing the core porosity with the acoustic wave, neutron, and density porosities of 49 wells, it is found that the correlation between the acoustic wave porosity and the core porosity is the best. Since the density curve is greatly affected by lithology, a part of the curve will reflect the lithology information, and the calculated porosity is not as accurate as the acoustic wave. Therefore, the acoustic wave curve is selected to establish the porosity evaluation model. There is a positive correlation linear relationship between the acoustic wave and the porosity, and the formula is y = 0.7614x - 42.202, and the correlation coefficient is 0.8376, with a relatively high correlation.

[0090] Through the established crossplot of the acoustic wave and the porosity, the acoustic wave curve can be input to calculate the porosity curve. The acoustic wave curve is obtained through logging, and the calculated porosity curve can be used to distinguish the fluid properties and can also be used as a calculation parameter for the permeability parameter.

[0091] Figure 3 The figure shows a schematic diagram of the correlation between the porosity curve and the permeability curve measured by the method according to this solution. In the Carboniferous of Tahe, the physical properties of different lithologies in different blocks vary greatly. In order to accurately calculate the pore-permeability parameters, it is necessary to establish the pore-permeability evaluation model by dividing into zones and lithologies. On the basis of core positioning and overburden pressure correction of the Carboniferous core pore-permeability data, the pore-permeability evaluation model of the Carboniferous is established by dividing into zones and lithologies. There is a positive correlation exponential relationship between the porosity and the permeability, and the formula is y = 0.0099·e 0.5592x , and the correlation coefficient is 0.89, with a relatively high correlation.

[0092] Through the established crossplot of the porosity and the permeability, the porosity curve can be input to calculate the permeability curve. The porosity curve is obtained by calculating the acoustic wave logging curve, and the calculated permeability curve can be used to distinguish the fluid properties and can also be used as an important calculation parameter for production capacity prediction.

[0093] At the site, gas logging shows that the Kalashayi Formation was drilled at depths of 5025 - 5029m, 5043 - 5047m, 5061 - 5064.5m, 5076 - 5079.5m, 5120.5 - 5123.5m, 5134.5 - 5137.5m, 5150 - 5154m, 5174 - 5177m, and 5268 - 5272m. Using the productivity prediction technology for complex lithology hydrocarbon reservoirs, 30m of relatively pure sandstone with a sand content of over 90% was accurately identified first. By establishing logging interpretation charts and logging interpretation standards for complex lithology hydrocarbon reservoirs by lithology, this layer was identified as an oil and gas layer without water. Through the calculation model of complex lithology porosity and permeability, the porosity of this layer was calculated to be 12% and the permeability was 68md, with good physical properties. Combining with the gas logging data of mud logging, through the productivity prediction model of complex lithology, the daily oil production of this well was calculated to be 280t, predicting good oil and gas production. After completion testing, the actual daily oil production was 288t without water. The productivity of this well predicted by the productivity prediction technology for complex lithology hydrocarbon reservoirs is basically the same as the productivity obtained from actual testing, with a very small error, indicating good application effect of the productivity prediction technology for lithology hydrocarbon reservoirs.

[0094] In addition, through on-site tests of 3 new wells in the Carboniferous Kalashayi Formation in the northern work area of Tahe Oilfield, the productivities of the 3 new wells were calculated according to the productivity prediction method for complex lithology hydrocarbon reservoirs in this scheme. High industrial production was obtained in all completion tests, and the predicted productivity is basically the same as the productivity obtained from actual testing. In addition, after verification by 30 old wells, the productivity prediction results are in line with the actual test productivity in 25 well layers, and do not match in 5 well layers, with a coincidence rate of 84.8%, indicating a relatively high accuracy.

[0095] This embodiment also discloses a computer-readable storage medium and a computer device. The computer-readable storage medium stores computer-executable instructions, which are used to implement the above method for predicting the productivity of complex lithology hydrocarbon reservoirs when executed by a processor.

[0096] The computer device includes a processor and a memory. The memory stores a computer program, and the computer program is loaded and executed by the processor to implement the above method for predicting the productivity of complex lithology hydrocarbon reservoirs.

[0097] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0098] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the appended claims.

Claims

1. A method for predicting the productivity of complex lithology hydrocarbon reservoirs, characterized in that, It includes the following steps: S1. Discriminate the specific lithology of the complex lithology sand layer, identify the specific lithology of the complex lithology sand layer, quantitatively calculate the proportion of various lithology components, and output the profile data of the complex lithology sand layer; S2. Respectively establish logging interpretation charts and logging interpretation standards for complex lithology hydrocarbon reservoirs for different lithology components, identify the fluid properties of complex lithology hydrocarbon reservoirs, and quantitatively calculate the proportion of various fluids, and output the pore fluid distribution profile data; S3. On the basis of core hole permeability data core positioning and overburden pressure correction, respectively establish the correlation between the acoustic wave curve and the porosity curve for different lithology components, establish the correlation between the porosity curve and the permeability curve, and output the porosity and permeability calculation models of the complex lithology sand layer; S4. Adopt the principle of heterogeneous assimilation and homogeneous differentiation of the analogy method, analyze the heterogeneous assimilation of the main variables and sub-variables of the high-yield hydrocarbon reservoir parameter changes, and differentiate the presence and size of variables of porosity, permeability, resistivity, reservoir thickness, maximum total hydrocarbon value, and maximum C1 value. Calculate the fluid scale of the complex lithology hydrocarbon reservoir through the main variable and sub-variable multi-parameter integrated analogy algorithm; S5. Based on the qualitative discrimination of the fluid properties of the complex lithology hydrocarbon reservoir and the quantitatively calculated fluid scale, output the production capacity prediction of the complex lithology hydrocarbon reservoir.

2. The method for predicting the productivity of complex lithology hydrocarbon reservoirs according to claim 1, wherein In step S1, the method for discriminating the specific lithology of the complex lithology sand layer includes: Conventional curve qualitative identification method, which identifies lithology based on the different response characteristics of conventional curves on different lithologies; Imaging logging qualitative identification method, which identifies lithology based on the different display characteristics of imaging logging on different lithologies; Lithology chart quantitative identification method, which identifies lithology based on the different lithology chart areas where the characteristic points of different lithologies fall.

3. A method for predicting the productivity of complex lithology hydrocarbon reservoirs according to claim 1, characterized in that, In step S1, the method for outputting the complex lithology profile data is: First, qualitatively determine which lithology components are in the complex lithology sand layer; Then, according to the different lithology components, select different processing parameters in the THsand processing program to process the logging curves, and the processed results are displayed using different lithology profiles according to the specific lithology of the complex lithology sand layer.

4. The method for predicting the productivity of a complex lithology hydrocarbon reservoir according to claim 1, characterized in that, In step S2, the method for identifying the fluid properties of the complex lithology hydrocarbon reservoir includes: Complex lithology fluid property qualitative identification method, which identifies based on the resistivity, total hydrocarbon value, total hydrocarbon curve morphology, single peak of nuclear magnetic T2 spectrum, and differential spectrum characteristics of oil and gas layers, oil-water layers, oil-bearing water layers, and water layers; Complex lithology fluid property quantitative identification method: Sort out the data of all current test wells, conduct logging and gas logging analysis on complex lithology test wells, select the most representative depth points, re-read all test layers, and draw logging interpretation charts and gas logging interpretation charts; Then, establish fluid identification charts for different lithology components with different petrophysical parameters respectively.

5. The method for predicting the productivity of a complex lithology oil and gas reservoir according to claim 1, characterized in that In step S3, in order to accurately calculate the pore permeability parameters, it is necessary to establish pore permeability evaluation models by region and lithology.

6. The method for predicting the productivity of a complex lithology oil and gas reservoir according to claim 1, characterized in that In step S4, by utilizing the analogical assimilation of the main variable C1 and the inter-class discriminability of the sub-variables C2, C3, iC4, nC4, iC5, and nC5, a multi-parameter integrated analogical algorithm is carried out to calculate and fit the functional changes of the main variable and the aggregate index of different sub-variables at multiple levels, and the Koc and Kw fluid identification indicators and the separation feature space are constructed: Kw = C1 / (C2 + C3 + iC4 + nC4 + iC5 + nC5) In the formula, C1, C2, C3, iC4, nC4, iC5, and nC5 are the data recorded during logging of methane, ethane, propane, isobutane, n-butane, isopentane, and n-pentane in the gas logging data. Koc: oil and gas abundance index, Kw: represents the wet-dry index; Taking Koc as the ordinate and Kw as the abscissa, a two-dimensional slope space model is constructed, and the spatial separation models of the oil-water coexistence zone, oil zone, gas zone, and water zone of the three-phase fluid are characterized by the spatial distribution laws of the Koc-Kw indicators of typical oil layers, gas layers, water layers, and oil-water coexistence layers.

7. The method for predicting the productivity of a complex lithology oil and gas reservoir according to claim 6, wherein When predicting the productivity of complex lithology hydrocarbon reservoirs, porosity, permeability, resistivity, reservoir thickness, maximum total hydrocarbon value, maximum C1 value are obtained from actual logging and mud logging data, and an empirical coefficient m is obtained from the actual measurement data. The productivity prediction formula for the complex lithology hydrocarbon reservoir can be approximately expressed as: q = m·f(h, φ, k, R, Koc, Kw) In the formula, q is the predicted productivity, m is the empirical coefficient, h is the effective thickness of the reservoir, φ is the effective porosity of the reservoir, k is the effective permeability of the reservoir, R is the resistivity of the reservoir, Koc is the oil and gas abundance index, and Kw is the wet-dry index.

8. The method for predicting the productivity of a complex lithology oil and gas reservoir according to claim 7, characterized in that The empirical coefficient m needs to be set according to the test productivity of the oil and gas well, the relationship between the production of the oil and gas well and the logging parameters is fitted, and the empirical coefficient m is determined according to the obtained fitting formula.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method for predicting the productivity of a complex lithology hydrocarbon reservoir according to any one of claims 1 to 8.

10. A computer device, characterized in that, The computer device includes a processor and a memory, the memory stores a computer program, and the computer program is loaded and executed by the processor to implement the method for predicting the productivity of a complex lithology hydrocarbon reservoir according to any one of claims 1 to 8.

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