Methods and devices for identifying oil and water properties in low-contrast reservoir logging

Abstract

This invention provides a method and apparatus for identifying oil-water properties in low-contrast reservoir logging, belonging to the field of petroleum exploration and development technology. The method includes: selecting eight lateral resistivity curves that are relatively sensitive to the lithology of the low-contrast reservoir based on its lithology type; calculating the porosity curve of the pure rock skeleton of the reservoir based on the density logging curve; constructing a discrimination curve for the low-contrast reservoir based on the eight lateral resistivity curves and the porosity curve of the pure rock skeleton; identifying the reservoir based on the discrimination curve; and identifying the oil-water properties of the reservoir using the corresponding deep resistivity curve and density curve. This invention solves the problem of difficult effective reservoir identification and improves the accuracy of reservoir oil-water property identification.

Description

Technical Field

[0001] This invention relates to the field of petroleum exploration and development technology, and specifically to a method and apparatus for identifying oil and water properties in low-contrast reservoir logging. Background Technology

[0002] In oil exploration and development, resistivity logging and three-porosity logging (sonic, neutron, and density) methods are commonly used to identify the fluid properties of oil and gas reservoirs. Resistivity logging and three-porosity logging are easily affected by non-fluid factors such as lithology, wellbore conditions, and mud intrusion, especially in low-contrast reservoirs developed in basins—complex reservoirs characterized by low porosity, low resistivity, and low saturation. These reservoirs exhibit unique and variable logging responses, making it difficult to identify the oil-water properties of the reservoir.

[0003] First, accurate reservoir identification is a prerequisite for identifying reservoir oil and water properties. Basin strata are generally rich in various minerals such as calcite, carbonaceous fragments, chlorite, biotite, and zircon, and contain various special lithologies such as high-GR (gamma) sandstone and calcareous cemented sandstone, blurring the boundary between reservoirs and non-reservoirs. If reservoir identification is based solely on natural gamma or three-porosity curve response characteristics, while ignoring the presence of these special lithologies, misidentification of interlayers and reservoirs can occur, thus affecting the identification of fluid properties.

[0004] Secondly, due to the complex formation conditions, the oil and gas charging of low-contrast reservoirs is generally insufficient, and production tests show almost no pure oil or pure water layers. There are special phenomena such as high water-resistant layers and low oil-resistant layers. Traditional reservoir fluid interpretation and classification methods are not applicable to the study of low-contrast reservoirs, making it difficult to accurately identify oil and water properties (fluid properties). Summary of the Invention

[0005] To address the aforementioned problems, this invention provides a method and apparatus for identifying oil and water properties in low-contrast reservoir logging.

[0006] The low-contrast reservoir logging oil-water property identification method provided by this invention includes: Based on the lithology type of the low-contrast reservoir, select eight lateral resistivity curves that are relatively sensitive to the lithology of the reservoir. The porosity curve of the pure rock skeleton of the reservoir is calculated based on the density logging curve; Based on the eight lateral resistivity curves and the porosity curves of the pure rock skeleton of the reservoir, a discrimination curve for low-contrast reservoirs is constructed. Reservoirs in the oil reservoir are identified based on the discrimination curve of low-contrast reservoirs; Based on the identified reservoir, the oil-water properties of the reservoir are identified using the corresponding deep resistivity curve and density curve.

[0007] In this embodiment of the invention, based on the lithological type of the low-contrast reservoir, eight lateral resistivity curves that are relatively sensitive to the lithology of the reservoir are selected, including: High-GR sandstone and fine sandstone were identified as reservoirs, and eight lateral resistivity curves, which are relatively sensitive to high-GR sandstone and fine sandstone, were selected.

[0008] In this embodiment of the invention, the porosity curve of the pure rock skeleton of the reservoir is calculated based on the density logging curve, including: calculating the porosity value of the pure rock skeleton of the reservoir based on the measured value of the density logging, the pore fluid density value and the rock skeleton density value, and obtaining the porosity curve of the pure rock skeleton of the reservoir based on the porosity value of the pure rock skeleton of the reservoir.

[0009] In this embodiment of the invention, based on the eight lateral resistivity curves and the porosity curve of the pure rock skeleton of the reservoir, the expression for the discrimination curve of the low-contrast reservoir is constructed as follows: LITH = φ DEN / LL8×N; Wherein, LITH represents the discrimination curve value of low-contrast reservoirs. φ DEN LL8 represents the porosity value of the pure rock skeleton of the reservoir, LL8 represents the lateral resistivity, and N represents the reservoir region constant.

[0010] In this embodiment of the invention, identifying the reservoir of an oil reservoir based on the discrimination curve of a low-contrast reservoir includes: identifying the reservoir of an oil reservoir based on the magnitude of the discrimination curve value of the low-contrast reservoir.

[0011] In this embodiment of the invention, before identifying the oil-water properties of the reservoir, the differences in the response characteristics of resistivity and porosity relative to different fluids are analyzed under the calibration of single-well oil test results. Based on the analysis results, density curve and deep resistivity curve are selected as sensitive curves for oil-water identification.

[0012] In this embodiment of the invention, based on the identified reservoir, the oil-water properties of the reservoir are identified using the corresponding deep resistivity curve and density curve, including: At a certain scale, the deep resistivity curve and the density curve are overlaid; The oil-water properties of the reservoir are determined by the size and shape of the overlapping area between the deep resistivity curve and the density curve.

[0013] In this embodiment of the invention, the method further includes: Based on industrial oil well standards and statistical results of single-layer oil testing data, production standards for reservoir oil and water properties are determined according to the daily oil production, daily fluid production, and water cut of the oil wells. Among them, reservoir oil and water properties include: oil layer, oil-water co-layer, oil-water-bearing layer, and dry layer.

[0014] In this embodiment of the invention, based on the identified reservoir, the oil-water properties of the reservoir are identified using the corresponding deep resistivity curve and density curve, including: By utilizing the deep resistivity and density curves corresponding to the reservoir, and combining them with production standards for the oil-water properties of the reservoir, the oil layer, oil-water co-layer, oil-water-bearing layer, and dry layer of the reservoir can be identified.

[0015] The present invention also provides a low-contrast reservoir logging oil-water property identification device, comprising: a reservoir identification module and an oil-water property identification module; The reservoir identification module is used to select an eight-lateral resistivity curve that is relatively sensitive to the lithology of the low-contrast reservoir based on the lithology type of the reservoir; calculate the porosity curve of the pure rock skeleton of the reservoir based on the density logging curve; construct a discrimination curve for the low-contrast reservoir based on the eight-lateral resistivity curve and the porosity curve of the pure rock skeleton of the reservoir; and identify the reservoir of the oil reservoir based on the discrimination curve of the low-contrast reservoir. The oil-water property identification module is used to identify the oil-water properties of a reservoir based on the reservoir identification module and by utilizing the deep resistivity curve and density curve corresponding to that reservoir.

[0016] In this embodiment of the invention, the oil-water property identification module determines the oil-water properties of the reservoir based on the size and shape of the overlapping area of ​​the deep resistivity curve and the density curve.

[0017] The present invention also provides a computer device, comprising: Memory, which stores computer programs; A processor is used to execute the computer program to implement the above-described method for identifying oil and water properties in low-contrast reservoir logging.

[0018] The present invention also provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the above-described method for identifying oil and water properties in low-contrast reservoir logging.

[0019] This invention utilizes porosity curves and eight-lateral resistivity curves to construct a discrimination curve for low-contrast reservoirs. This discrimination curve is used to identify reservoirs in oil reservoirs, solving the problem of difficulty in identifying effective reservoirs. Then, based on reservoir identification, deep resistivity curves and density curves that are sensitive to oil-water properties are selected to identify the oil-water properties of the reservoirs, improving the accuracy of reservoir oil-water property identification and avoiding the drawbacks of easy misjudgment and low identification accuracy in the process of determining the oil-water properties of low-contrast oil reservoirs.

[0020] Other features and advantages of the technical solution of the present invention will be described in detail in the following detailed embodiments section. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of the low-contrast reservoir logging oil-water property identification method provided by an embodiment of the present invention; Figure 2 This is a reservoir identification map in a specific embodiment of the present invention; Figure 3 This is an oil layer identification diagram in a specific embodiment of the present invention; Figure 4 This is an oil-water co-layer identification diagram in a specific embodiment of the present invention; Figure 5 This is an oil-water layer identification diagram in a specific embodiment of the present invention; Figure 6 This is a dry layer identification diagram in a specific embodiment of the present invention; Figure 7 This is a block diagram of a low-contrast reservoir logging oil-water property identification device provided in an embodiment of the present invention. Detailed Implementation

[0022] To make the technical solutions and advantages of the embodiments of the present invention clearer, the exemplary embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not an exhaustive list of all embodiments. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0023] This invention addresses the difficulty in identifying oil-water properties in low-contrast reservoirs. First, it constructs a discrimination curve for low-contrast reservoirs using porosity curves and eight-lateral resistivity curves. This discriminant curve is then used to identify the reservoir layers, solving the problem of difficulty in identifying effective reservoirs. Then, based on reservoir identification, it prioritizes deep resistivity and density curves, which are sensitive to oil-water properties, to further identify the oil-water properties of the reservoir. This avoids the drawbacks of easy misjudgment and low accuracy in determining the oil-water properties of low-contrast reservoirs.

[0024] Figure 1 This is a flowchart of a low-contrast reservoir logging oil-water property identification method provided by an embodiment of the present invention. Figure 1 As shown in this embodiment, the method for identifying oil-water properties in low-contrast reservoir logging includes the following steps: S101, based on the lithology type of the low-contrast reservoir, select the eight lateral resistivity curves that are relatively sensitive to the lithology of the reservoir. S102, the porosity curve of the pure rock skeleton of the reservoir is calculated based on the density logging curve; S103, based on the eight lateral resistivity curves and the porosity curve of the pure rock skeleton of the reservoir, a discrimination curve for low-contrast reservoirs is constructed. S104, Identify reservoirs in the oil reservoir based on the discrimination curve of low-contrast reservoirs; S105. Based on the identified reservoir, the oil-water properties of the reservoir are identified using the deep resistivity curve and density curve corresponding to the reservoir.

[0025] In step S101 above, based on core observation and thin section analysis results, high-GR (gamma-ray) sandstone and fine sandstone were identified as reservoirs, while calcareous cemented sandstone, mudstone, and siltstone were identified as interlayers. Specifically, mudstone was identified as argillaceous interlayers, and calcareous cemented sandstone and siltstone as physical property interlayers. Eight-lateral resistivity curves and density logging curves, which are sensitive to the lithology (i.e., high-GR sandstone and fine sandstone) and physical properties of the reservoirs, were selected as the basis for reservoir identification.

[0026] In step S102 above, the porosity curve of the pure rock skeleton of the reservoir is calculated based on the density logging curve. Specifically, the porosity value of the pure rock skeleton of the reservoir is calculated based on the measured value of the density logging, the pore fluid density value, and the rock skeleton density value. The porosity curve of the pure rock skeleton of the reservoir is obtained based on the porosity value of the pure rock skeleton of the reservoir.

[0027] In step S103 above, based on the eight lateral resistivity curves and the porosity curve of the pure rock skeleton of the reservoir, the expression for constructing the discrimination curve of the low-contrast reservoir is as follows: LITH =φ DEN / LL8×N; Where LITH represents the discrimination curve value of the low-contrast reservoir, φ DEN % represents the porosity value of the pure rock skeleton of the reservoir, in units of %; LL8 represents the lateral resistivity, in units of Ω•m; N represents the reservoir region constant.

[0028] In step S104 above, the reservoir of the oil reservoir is identified based on the discrimination curve of the low-contrast reservoir. Specifically, the reservoir of the oil reservoir is identified based on the magnitude of the discrimination curve value (LITH) of the low-contrast reservoir. For example, when the LITH value is greater than a certain threshold value, the rock formation area corresponding to that LITH value is considered a reservoir.

[0029] In step S105 above, under the calibration of single-well oil test results, the differences in the response characteristics of resistivity and porosity relative to different fluids are analyzed. It is determined that the high-resolution density curve reflects changes in reservoir lithology and physical properties better than acoustic and neutron curves. The deep resistivity curve, compared to the medium and shallow resistivity curves, is less affected by mud intrusion and mainly reflects the oil-bearing properties of the undisturbed reservoir. Therefore, the density curve and deep resistivity curve are preferred as sensitive curves for oil-water identification. Using the density curve and deep resistivity curve, the deep resistivity curve is overlaid with the density curve at a certain scale. Based on the size and shape of the overlap area between the deep resistivity curve and the density curve, the oil-water properties of the reservoir are determined.

[0030] To establish a standard for identifying the oil-water properties of low-contrast reservoirs, it is necessary to determine production standards for reservoir oil-water properties. Therefore, based on industrial well standards and statistical results of single-layer well testing data, and according to daily oil production and water cut, production standards for oil-bearing layers, oil-water co-layers, oil-water-bearing layers, and dry layers are formulated as calibration bases for identifying logging fluid properties. Under the calibration of single-well well testing results, the differences in the response characteristics of porosity and resistivity curves to different fluids are analyzed, and density and deep resistivity are selected as sensitive curves for oil-water identification. The logging curves of density and deep resistivity are overlapped at a certain scale; the size of the overlap area reflects the oil-bearing characteristics of the reservoir, serving as the main basis and reference for determining the oil-water properties of the reservoir. Based on the production standards of well testing and the identification results of low-contrast reservoir oil-water properties, and according to the size and shape of the overlap area of ​​density and deep resistivity curves, and the judgment standards for low-contrast reservoirs, a standard for identifying the oil-water properties of low-contrast reservoirs is established. This standard comprehensively distinguishes the properties of different types of fluids in single wells to identify oil-bearing layers, oil-water co-layers, oil-water-bearing layers, and dry layers.

[0031] This invention addresses the challenge of identifying oil and water properties in low-contrast reservoirs. First, it constructs reservoir identification curves and discrimination criteria using density, porosity, and resistivity curves, solving the problem of effective reservoir identification. Based on reservoir identification, it prioritizes logging curves sensitive to oil and water properties, overlapping them at a specific scale. The size of the overlap area reflects the reservoir's oil-bearing characteristics, serving as the primary basis and reference for determining the reservoir's oil and water properties. Finally, based on oil testing production standards, the overlap area of ​​density and deep resistivity curves, reservoir judgment criteria, and porosity lower limit standards, a comprehensive standard for identifying oil and water properties in low-contrast reservoirs is established. This standard comprehensively discriminates different types of fluid properties in single wells, demonstrating high consistency with oil testing results. This represents an innovative research method in the field of low-contrast reservoir exploration and development. This invention overcomes the shortcomings of conventional logging techniques in identifying reservoir and fluid properties in low-contrast reservoirs, such as susceptibility to misjudgment and low accuracy. It can significantly improve the accuracy of oil and water identification in low-contrast reservoirs and similar low-porosity tight reservoirs, thereby guiding oilfield exploration and development. This invention has promising applications in reservoir identification and fluid discrimination in low-permeability tight oil reservoirs.

[0032] In one specific embodiment, taking a basin oilfield as an example, the technical solution of the present invention will be described in detail.

[0033] 1.1 Determining the lithological type of low-contrast reservoirs High-GR fine sandstone layers exhibit high GR, low DEN, and low LL8 logging response characteristics, similar to mudstone logging response characteristics. However, core observation and thin section analysis show that their mud content is not significantly different from ordinary fine sandstone. Rich in zircon, they have a high GR value, but good physical properties (φ>15%). Oil and gas shows are predominantly oil-permeable, indicating a reservoir rather than a mudstone interlayer. Fine sandstone layers exhibit medium-low DEN, low GR, and medium-high LL8 characteristics, with good oil-bearing and physical properties, indicating a reservoir. Calcareous cemented sandstone has extremely low GR and high LL8 values, exhibiting a finger-like pattern, similar to the logging response characteristics of conventional oil-saturated sandstone reservoirs, easily misidentified as a reservoir. However, its physical properties are poor (φ<7%), it contains no oil, and it is a physical property-isolated interlayer. Mudstone exhibits high GR, high DEN, and medium LL8 characteristics, with extremely poor physical properties and oil-bearing properties, indicating a mudstone interlayer.

[0034] In summary, high-GR sandstone and fine sandstone were identified as reservoirs. Calcareous cemented sandstone, mudstone, and siltstone were identified as interlayers, with mudstone being the argillaceous interlayer and calcareous cemented sandstone and siltstone being the physical property interlayers.

[0035] 1.2 Constructing low-contrast reservoir discrimination curves and standards Calculate the porosity of the pure rock skeleton in the reservoir using density curves. φ DEN Its specific expression is: ; In the formula, ρ ma Density of the rock skeleton, in g / cm³ 3 ;ρ b These are density logging measurements, in g / cm³. 3 ;ρ f Pore ​​fluid density, in g / cm³ 3 Under conditions of clastic rock formations and freshwater slurry drilling fluid, the rock skeleton density ρ ma It is 2.65 g / cm³ 3 pore fluid density ρ f 1.0 g / cm 3 .

[0036] Eight lateral resistivity curves sensitive to reservoir lithology and physical properties were selected, along with porosity curves of the pure rock skeleton of the reservoir calculated using density curves, to construct the reservoir discrimination curve LITH, the specific expression of which is as follows: LITH = φ DEN / LL8×N; In the formula, φ DEN 1 represents the density and porosity of the pure rock skeleton, expressed as %; LL8 represents the lateral resistivity, expressed as Ω•m; N is the regional constant, for example, N=100 in the Red River region.

[0037] Based on the LITH value of the reservoir discrimination curve, a discrimination standard for low-contrast reservoirs in oilfields is established to classify reservoirs. The criteria for distinguishing between reservoirs and interlayers are as follows: in the range where the LITH value is between 0 and 0.5, the lithology is mainly calcareous cemented sandstone, mudstone, and siltstone, with a porosity of less than 10%, and these are interlayers; in the range where the LITH value is between 0.5 and 1.0, the lithology is mainly high-GR sandstone and fine sandstone, with a porosity greater than 10%, and these are reservoirs.

[0038] Figure 2 This is a reservoir identification map in a specific embodiment, such as... Figure 2 As shown, the LITH values ​​at the black boxes in the lithology identification curves are all greater than 0.5, and are identified as reservoirs.

[0039] 2.1 Determining production standards for reservoir oil and water properties Given the unique characteristics of low-contrast reservoirs, no pure oil or pure water layers were observed during oil testing and production, rendering conventional oil-water production standards inapplicable. Based on industrial well standards and statistical results of single-layer oil testing data in the study area, and according to the daily oil production, daily fluid production, and water cut of 76 vertical wells, production standards for oil layers, oil-water co-containment layers, oil-water-bearing layers, and dry layers were established as calibration criteria for identifying well logging fluid properties.

[0040] The criteria for distinguishing between different properties of fluids are as follows: Oil reservoir: Daily oil production greater than 3 tons, water content <30%; Oil and water coexisting: daily oil production less than 3t, water content ∈ (30%, 80%); Oil-bearing aquifer: Daily liquid production greater than 2 tons, water cut >80%; Dry layer: Daily liquid production is less than 1 t.

[0041] Right now:

[0042] 2.2 Optimization of Sensitive Recognition Curves for Oil-Water Properties Based on the calibration of single-well oil testing results, the differences in the response characteristics of porosity and resistivity curves to different fluids were analyzed. It was determined that high-resolution density curves are more effective than acoustic and neutron curves in reflecting changes in reservoir lithology and physical properties. Deep resistivity curves, compared to medium and shallow resistivity curves, are less affected by mud intrusion and primarily reflect the oil-bearing properties of the undisturbed reservoir. Therefore, density curves and deep resistivity curves are preferred as sensitive curves for oil-water identification.

[0043] 2.3 Constructing a method for identifying reservoir oil-water properties Using density and deep resistivity logging curves, the overlying mudstone layers adjacent to the reservoir are superimposed at a certain scale, requiring the thickness of the overlying or underlying mudstone layers to be no less than 3 meters. The size and shape of the overlapping area at the reservoir reflect the oil-bearing characteristics of the reservoir and serve as the main method and basis for determining the oil-water properties of the reservoir.

[0044] The specific steps are as follows: The first step is to set the deep resistivity curve scale to a logarithmic scale, with the left scale value equal to 2 Ω•m and the right scale value equal to 200 Ω•m; the density curve scale is set to a linear scale, with the initial left scale value equal to 2 g / cm³. 3 The right-hand scale value is equal to 3g / cm. 3 The plotting path for loss identification is set to 5 equal parts, which serves as a reference for judging the size of the curve overlap area; The second step is to select a mudstone layer longer than 3m, ensuring that the deep resistivity and density curves coincide. If the two curves do not overlap in the mudstone layer, the deep resistivity curve should be 2g / cm² to the left. 3 Right 200g / cm 3 The scale remains unchanged, while the density curve scale is adjusted by adding or subtracting a certain value, within ±0.02 g / cm³. 3 To adjust the step size, but keep the difference between the left and right scale values ​​at 1.0 g / cm. 3 The deep resistivity curve and density curve remain unchanged until they substantially overlap in the selected mudstone layer. The third step involves determining the overlap area between the resistivity and density curves based on the LITH (Liquidity-Induced Threat) identification results. For reservoirs with a LITH curve greater than 0.5, the overlap area is determined. The better the oil content of the reservoir, the larger the overlap area; the lower the oil content, the smaller the overlap area; and if the reservoir contains no oil, there is no overlap area.

[0045] 3.1 Establish identification criteria for oil-water properties in low-contrast reservoirs, including the following identification criteria for oil layers, oil-water co-layers, oil-water-bearing layers, and dry layers: Oil layer: Daily oil production from oil testing is greater than 3t, water content is less than 30%. Calibrated LITH curve is greater than 0.5, porosity is greater than 10%, deep resistivity and density curves have a large overlap area, and the area occupied in the fluid identification plot is greater than 1 grid. Oil-water co-layer: The daily oil production in the oil test is less than 3t, and the water content is between 30% and 80%. The calibrated LITH curve is greater than 0.5, the porosity is greater than 10%, the overlap area of ​​the deep resistivity and density curves is moderate, and the area occupied in the fluid identification plot is between 0.5 and 1 grid. Oil-bearing water layer: Daily oil production in the test is greater than 2t, and the water content is >80%. The calibrated LITH curve is greater than 0.5, the porosity is greater than 10%, and the overall overlap area of ​​the deep resistivity and density curves is small, occupying less than 0.5 grids in the fluid identification plotting channel; Dry layer: The daily liquid production of the test oil is less than 1t, the calibration LITH curve is greater than 0.5, the porosity is less than 10%, the deep resistivity and density curves have no overlapping area, and the area occupied by the fluid identification plot is approximately linear.

[0046] Based on the overlap area of ​​density and deep resistivity curves, and in conjunction with well logging curve standards, LITH reservoir judgment standards, porosity lower limit standards, and oil testing standards, a standard for identifying the oil-water properties of low-contrast reservoirs is established, namely:

[0047] Reference Figures 3 to 6 The identification criteria for oil layers, oil-water co-layers, oil-water-containing layers, and dry layers are as follows: 1) Oil layer: Layers 12(1) and 12(3) of well X1 have a calculated LITH curve greater than 1.0, an average porosity greater than 15%, and a large overlap area between the deep resistivity curve and density curve. The area occupied in the fluid identification plot is greater than 1.5 grids, indicating that the oil-water properties identify it as an oil layer. Actual oil testing showed a daily fluid production of 6.8t and a daily oil production of 4.8t, with a water cut of 25%. The production conclusion is that it is an oil layer, and the identification results are as follows: Figure 3 As shown.

[0048] 2) Oil and water co-layers: In well X2, layers 15(1) and 15(2) have a calculated LITH curve greater than 1.0, an average porosity of approximately 12%, and a moderate overlap area between the deep resistivity and density curves. The area occupied in the fluid identification plot is approximately 0.6 grids, indicating that the oil and water properties are co-layered. Actual oil testing yielded 6.1t of fluid and 2.8t of oil per day, with a water cut of 54%. The production conclusion is that the oil and water are co-layered, and the identification results are as follows: Figure 4 As shown.

[0049] 3) Oil-water-bearing layer: Layer 3 of well X3 has a calculated LITH curve greater than 0.7, an average porosity of approximately 10%, and a small overlap area between the deep resistivity and density curves, occupying approximately 0.3 grid squares in the fluid identification plot. Based on this, it is identified as an oil-water-bearing layer. Actual oil testing yielded 4.1 tons of fluid and 0.8 tons of oil per day, with a water cut of 81%. The production conclusion is that it is an oil-bearing layer. The identification results are as follows: Figure 5 As shown.

[0050] 4) Dry Layers: Layers 11, 12, 13, and 14 of Well X4 have calculated LITH curves greater than 0.6, an average porosity of approximately 7%, and a large overlap area between the deep resistivity and density curves. The area occupied by these curves in the fluid identification plot is approximately linear, indicating dry layers. Actual oil testing yielded 0.5t of fluid and 0.1t of oil per day, with a water cut of 80%. The production conclusion is that these are dry layers. The identification results are as follows: Figure 6 As shown.

[0051] This invention also provides a low-contrast reservoir logging oil-water property identification device. For example... Figure 7 As shown, the low-contrast reservoir logging oil-water property identification device includes a reservoir identification module and an oil-water property identification module. The reservoir identification module selects an eight-lateral resistivity curve that is relatively sensitive to the lithology of the low-contrast reservoir based on its lithology type; calculates the porosity curve of the pure rock skeleton of the reservoir based on the density logging curve; constructs a discrimination curve for the low-contrast reservoir based on the eight-lateral resistivity curve and the porosity curve of the pure rock skeleton; and identifies the reservoir based on the discrimination curve of the low-contrast reservoir. The oil-water property identification module, based on the reservoir identified by the reservoir identification module, uses the corresponding deep resistivity curve and density curve to identify the oil-water properties of the reservoir.

[0052] Specifically, the reservoir identification module calculates the porosity value of the pure rock skeleton of the reservoir based on the measured values ​​from density logging, pore fluid density, and rock skeleton density. It then obtains the porosity curve of the pure rock skeleton based on this porosity value. Finally, it constructs a discrimination curve for low-contrast reservoirs based on the eight lateral resistivity curves and the porosity curve of the pure rock skeleton. The module identifies the reservoir based on the magnitude of the discrimination curve value for low-contrast reservoirs.

[0053] Specifically, the oil-water property identification module preferentially uses density curves and deep resistivity curves as sensitive curves for oil-water identification. Using density curves and deep resistivity curves, the deep resistivity curve is overlapped with the density curve at a certain scale. Based on the size and shape of the overlap area between the deep resistivity curve and the density curve, the oil-water properties of the reservoir are determined.

[0054] The specific details of the low-contrast reservoir logging oil-water property identification device can be understood by referring to the specific embodiments of the low-contrast reservoir logging oil-water property identification method described above, and will not be repeated here.

[0055] The present invention also provides a computer device, including: a memory and a processor, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the above-described method for identifying oil and water properties in low-contrast reservoir logging.

[0056] The present invention also provides a machine-readable storage medium storing computer program instructions thereon, which, when executed by a processor, implement the above-described method for identifying oil and water properties in low-contrast reservoir logging.

[0057] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0058] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0059] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0060] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0061] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and modifications.

Claims

1. A method for identifying oil-water properties in low-contrast reservoir logging, characterized in that, include: Based on the lithology type of the low-contrast reservoir, select eight lateral resistivity curves that are relatively sensitive to the lithology of the reservoir. The porosity curve of the pure rock skeleton of the reservoir is calculated based on the density logging curve; Based on the eight lateral resistivity curves and the porosity curves of the pure rock skeleton of the reservoir, a discrimination curve for low-contrast reservoirs is constructed. Reservoirs in the oil reservoir are identified based on the discrimination curve of low-contrast reservoirs; Based on the identified reservoir, the oil-water properties of the reservoir are identified using the corresponding deep resistivity curve and density curve.

2. The method for identifying oil-water properties in low-contrast reservoir logging according to claim 1, characterized in that, Based on the lithological type of the low-contrast reservoir, eight lateral resistivity curves that are relatively sensitive to the lithology of the reservoir are selected, including: High-GR sandstone and fine sandstone were identified as reservoirs, and eight lateral resistivity curves, which are relatively sensitive to high-GR sandstone and fine sandstone, were selected.

3. The method for identifying oil and water properties in low-contrast reservoir logging according to claim 1, characterized in that, The porosity curve of the pure rock skeleton of the reservoir is calculated based on the density logging curve, including: The porosity value of the pure rock skeleton in the reservoir is calculated based on the measured values ​​from density logging, pore fluid density, and rock skeleton density. The porosity curve of the pure rock skeleton in the reservoir is then obtained based on the porosity value.

4. The method for identifying oil-water properties in low-contrast reservoir logging according to claim 3, characterized in that, Based on the eight lateral resistivity curves and the porosity curves of the pure rock skeleton of the reservoir, the expression for the discrimination curve of the low-contrast reservoir is constructed as follows: LITH = φ DEN / LL8×N; Wherein, LITH represents the discrimination curve value of low-contrast reservoirs. φ DEN LL8 represents the porosity value of the pure rock skeleton of the reservoir, LL8 represents the lateral resistivity, and N represents the reservoir region constant.

5. The method for identifying oil and water properties in low-contrast reservoir logging according to claim 4, characterized in that, Reservoirs in oil reservoirs are identified based on the discrimination curves of low-contrast reservoirs, including: The reservoir is identified based on the magnitude of the discrimination curve value of the low-contrast reservoir.

6. The method for identifying oil and water properties in low-contrast reservoir logging according to claim 1, characterized in that, Before identifying the oil-water properties of the reservoir, the differences in the response characteristics of resistivity and porosity relative to different fluids were analyzed under the calibration of single-well oil test results. Based on the analysis results, density curve and deep resistivity curve were selected as sensitive curves for oil-water identification.

7. The method for identifying oil-water properties in low-contrast reservoir logging according to claim 1, characterized in that, Based on the identified reservoir, the oil-water properties of the reservoir are identified using the corresponding deep resistivity and density curves, including: At a certain scale, the deep resistivity curve and the density curve are overlaid; The oil-water properties of the reservoir are determined by the size and shape of the overlapping area between the deep resistivity curve and the density curve.

8. The method for identifying oil-water properties in low-contrast reservoir logging according to claim 1, characterized in that, The method further includes: Based on the statistical results of industrial oil well standards and single-layer oil testing data, the production standards for reservoir oil and water properties are determined according to the daily oil production, daily fluid production and water cut of the oil well. The reservoir oil-water properties include: oil layer, oil-water co-layer, oil-water layer, and dry layer.

9. The method for identifying oil-water properties in low-contrast reservoir logging according to claim 8, characterized in that, Based on the identified reservoir, the oil-water properties of the reservoir are identified using the corresponding deep resistivity and density curves, including: By utilizing the deep resistivity and density curves corresponding to the reservoir, and combining them with production standards for the oil-water properties of the reservoir, the oil layer, oil-water co-layer, oil-water-bearing layer, and dry layer of the reservoir can be identified.

10. A low-contrast reservoir logging oil-water property identification device, characterized in that, include: Reservoir identification module and oil-water property identification module; The reservoir identification module is used to select an eight-lateral resistivity curve that is relatively sensitive to the lithology of the low-contrast reservoir based on the lithology type of the reservoir; calculate the porosity curve of the pure rock skeleton of the reservoir based on the density logging curve; construct a discrimination curve for the low-contrast reservoir based on the eight-lateral resistivity curve and the porosity curve of the pure rock skeleton of the reservoir; and identify the reservoir of the oil reservoir based on the discrimination curve of the low-contrast reservoir. The oil-water property identification module is used to identify the oil-water properties of the reservoir based on the reservoir identified by the reservoir identification module, using the deep resistivity curve and density curve corresponding to the reservoir.

11. The low-contrast reservoir logging oil-water property identification device according to claim 10, characterized in that, The oil-water property identification module determines the oil-water properties of the reservoir based on the size and shape of the overlapping area between the deep resistivity curve and the density curve.

12. A computer device, characterized in that, include: Memory, which stores computer programs; A processor for executing the computer program to implement the low-contrast reservoir logging oil-water property identification method according to any one of claims 1-9.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor to implement the low-contrast reservoir logging oil-water property identification method according to any one of claims 1-9.

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