A rock resistivity spectrum testing method and joint testing device based on capillary pressure

Abstract

This article relates to the field of oil and gas field development, and in particular, to a capillary pressure-based rock resistivity spectrum testing method and joint measurement device. The method involves obtaining water-saturated resistivity spectrum data from a rock core; inputting the water-saturated resistivity spectrum data into a pre-established capillary pressure type prediction model to determine the capillary pressure curve type corresponding to the water-saturated resistivity spectrum data. The capillary pressure curve type is represented by the number of characteristic points on the capillary pressure curve and the pressure differential values ​​corresponding to the characteristic points; determining a pressure differential measurement sequence based on the characteristic points, including a minimum pressure differential value, a maximum pressure differential value, and a pressure differential threshold value; and measuring the resistivity spectrum data at each point in the pressure differential measurement sequence. This method predicts the capillary pressure curve type, reduces the number of pressure differential measurement points, and improves measurement efficiency. By combining resistivity and capillary pressure measurements, different resistivity spectrum curves are measured at different pressure measurement points. This provides more formation information than a single spectrum resistivity, which is beneficial to the development of future geological research.

Description

Technical Field

[0001] This article relates to the field of oil and gas field development, in particular to a rock resistivity spectrum testing method and joint testing device based on capillary pressure. Background Art

[0002] In the process of oil and gas reservoir exploration and development, geophysical logging is an important means of reservoir evaluation, and electrical logging technology is the main basis for quantitative evaluation of reservoir saturation.

[0003] In the existing technology, conventional resistivity logging is usually used to measure the resistivity of the formation, and the Archie formula is combined to evaluate the reservoir saturation information. Among them, conventional resistivity logging can be regarded as a single frequency point measurement, which cannot accurately evaluate mixed reservoirs such as low porosity, low permeability, low resistivity reservoirs and water-flooded layers. Permeability is another important physical property parameter of rocks. For conventional reservoirs, there is usually a positive correlation between porosity and permeability, that is, the greater the porosity, the greater the permeability of the rock. However, for mixed reservoirs under exploration and development, the corresponding porosity and permeability are less correlated, so other measurement methods are needed to evaluate the pore structure of the formation.

[0004] In order to solve the problem that ordinary resistivity in current technology cannot accurately evaluate mixed reservoirs, a rock resistivity spectrum testing method and joint testing device based on capillary pressure are needed. Summary of the Invention

[0005] To solve the above-mentioned problems of the prior art, the embodiments of this document provide a rock resistivity spectrum testing method and a joint testing device based on capillary pressure.

[0006] An embodiment of the present invention provides a rock resistivity spectrum testing method based on capillary pressure, the method comprising: obtaining water-saturated resistivity spectrum data of a rock core; inputting the water-saturated resistivity spectrum data into a pre-established capillary pressure type prediction model to determine the capillary pressure curve type corresponding to the water-saturated resistivity spectrum data, the capillary pressure curve type being represented by the number of characteristic points of the capillary pressure curve and the pressure differential values ​​corresponding to the characteristic points; determining a pressure differential measurement sequence based on the characteristic points, including a minimum pressure differential value, a maximum pressure differential value, and a pressure differential threshold value; and measuring the resistivity spectrum data of each point in the pressure differential measurement sequence.

[0007] According to one aspect of the embodiments of this document, the training process of the capillary pressure type prediction model is as follows: historical water-saturated resistivity spectrum data of different cores and historical capillary pressure curve data corresponding to the historical water-saturated resistivity spectrum sample data are obtained; a random number of feature points are selected from the historical capillary pressure curve data as initial feature points, wherein the initial feature points do not include the first and last endpoints of the historical capillary pressure curve, and the initial feature points include their corresponding water saturation and pressure difference values; the initial feature points and the historical water-saturated resistivity spectrum data are determined as a training sample set; an initial capillary pressure curve is constructed based on the training sample set; the error between the initial capillary pressure curve and the historical capillary pressure curve data is calculated; the number of initial feature points of the initial capillary pressure curve is iteratively adjusted based on the error and the maximum number of iterations, and the capillary pressure type prediction model is determined when the error meets a preset error value.

[0008] According to one aspect of the embodiments of this document, the iterative adjustment of the number of characteristic points of the initial capillary pressure curve based on the error and the maximum number of iterations to determine the capillary pressure type prediction model includes: determining whether the current number of iterations exceeds the maximum number of iterations; if not, further determining whether the error is greater than a preset error value; if the error is less than or equal to the preset error value, determining the minimum number of characteristic points of the initial capillary pressure curve, and determining the capillary pressure type prediction model corresponding to the minimum number of characteristic points; if the error is greater than the preset error value, adjusting the number of initial characteristic points, and re-executing the steps of iterating the initial capillary pressure curve and judging the error and the preset error value until the current number of iterations exceeds the maximum number of iterations; if so, adjusting the number of initial characteristic points, and re-executing the steps of iterating the initial capillary pressure curve and judging the error and the preset error value.

[0009] According to one aspect of the embodiments of this document, the step of adjusting the number of initial feature points and re-executing the step of determining the error and the preset error value includes: sequentially increasing the number of initial feature points and calculating the error between the initial capillary pressure curve and the historical capillary pressure curve data; or sequentially decreasing the number of initial feature points and calculating the error between the initial capillary pressure curve and the historical capillary pressure curve data.

[0010] According to one aspect of the embodiments herein, a pressure differential measurement sequence is determined based on the characteristic points and the pressure differential values. When each pressure differential measurement point in the pressure differential measurement sequence satisfies a pressure differential balance condition, the resistivity spectrum under the pressure differential balance condition is measured. The pressure differential balance condition includes a pressure differential balance time and a balanced liquid output.

[0011] According to one aspect of the embodiments of this document, the method further includes: preprocessing the historical water-saturated resistivity spectrum data, including: filtering and noise reduction, curve smoothing, and standardization.

[0012] An embodiment of the present invention provides a device for combined measurement of rock resistivity spectrum and capillary pressure, the device comprising a core holder, an electrical spectrum measurement system, and a hydraulic system. The core holder comprises upper and lower clamping pistons, a core clamping chamber, upper and lower liquid outlet pipes, upper and lower pressure relief valves, and a semi-permeable partition. The upper and lower pistons are connected to two power supply electrodes, and the side ends of the core chamber are connected to two measuring electrodes for clamping the core. The electrical spectrum measurement system comprises an impedance analyzer, power supply electrodes, and measuring electrodes for measuring resistivity spectrum data. The hydraulic system comprises an upstream pressure sensor, a downstream pressure sensor, a differential pressure sensor, a metering pump, a displacement pump, a confining pressure pump, and a pump controller for applying pressure differentials of varying magnitudes to the core.

[0013] According to one aspect of the embodiments of this document, the electrical spectrum measurement system is further configured to measure resistivity spectrum data of each characteristic point based on the number of characteristic points predicted and output by the capillary pressure type prediction model and the pressure difference values ​​corresponding to the characteristic points.

[0014] The embodiments of this document also provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the capillary pressure-based rock resistivity spectrum testing method is implemented.

[0015] The embodiments of this document further provide a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the capillary pressure-based rock resistivity spectrum testing method is implemented.

[0016] This scheme reduces the number of pressure difference measurement points and improves measurement efficiency by predicting the capillary pressure curve type. This paper combines resistivity with capillary pressure measurement to measure different resistivity spectrum curves at different pressure measurement points. This provides more stratigraphic information than a single spectrum resistivity, which is beneficial to the development of future geological research. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of this article or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of this article. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 The figure shows a flow chart of a rock resistivity spectrum testing method based on capillary pressure according to an embodiment of this invention;

[0019] Figure 2 Shown is a flow chart of a method for determining a capillary pressure type prediction model according to an embodiment of this invention;

[0020] Figure 3 Shown is a flow chart of a method for determining a capillary pressure type prediction model according to an embodiment of this invention;

[0021] Figure 4 FIG2 is a flow chart of a method for measuring resistivity spectrum data in a differential pressure measurement sequence according to an embodiment of the present invention;

[0022] Figure 5 FIG2 is a schematic diagram of the structure of a rock resistivity spectrum and capillary pressure joint measurement device according to an embodiment of this invention;

[0023] Figure 6 The figure shows a cross-sectional schematic diagram of a core holder according to an embodiment of this invention;

[0024] Figure 7A Shown is a schematic diagram of a capillary pressure curve of a pore structure in an embodiment of this invention;

[0025] Figure 7B Shown is a schematic diagram of a capillary pressure curve of another pore structure according to an embodiment of this invention;

[0026] Figure 8 The figure shows a schematic diagram of the structure of a computer device according to an embodiment of the present invention.

[0027] 501, displacement pump;

[0028] 502, confining pressure pump;

[0029] 503, metering pump;

[0030] 504, displacement pump suction bottle;

[0031] 505, confining pressure pump suction bottle;

[0032] 506. Metering pump suction bottle;

[0033] 507, pressure sensor at the upper end of the core chamber;

[0034] 508. Pressure sensor at the lower end of the core chamber;

[0035] 509, differential pressure sensor;

[0036] 510, pipeline control switch;

[0037] 511, temperature sensor;

[0038] 512. Pressure relief valve at the lower end of the core chamber;

[0039] 513. Displacement pump oil storage tank;

[0040] 514. Displacement pump water storage tank;

[0041] 515, data acquisition board;

[0042] 516. Computer display screen;

[0043] 517. Computer;

[0044] 518. Impedance analyzer;

[0045] 519. Constant temperature box;

[0046] 520. Manual control device for pressure pump;

[0047] 521, pressure relief valve at the upper end of the core chamber;

[0048] 522. Temperature sensor at the upper end of the core chamber;

[0049] 523, core holder;

[0050] 601, clamping knob at the upper end of the core chamber;

[0051] 602, upper power supply electrode;

[0052] 603, displacement port;

[0053] 604, upper exhaust port;

[0054] 605, core sample;

[0055] 606, confining pressure relief valve;

[0056] 607, semi-permeable partition;

[0057] 608, lower exhaust port;

[0058] 609, metering port;

[0059] 610, lower power supply electrode;

[0060] 611, clamping knob at the lower end of the core chamber;

[0061] 612, rubber sleeve;

[0062] 613. First measuring electrode;

[0063] 614. second measuring electrode;

[0064] 615, confining pressure input port;

[0065] 616, metal housing of the holder;

[0066] 802. Computer equipment;

[0067] 804, processor;

[0068] 806, memory;

[0069] 808, driving mechanism;

[0070] 810, input / output module;

[0071] 812. Input devices;

[0072] 814. Output device;

[0073] 816. Presentation equipment;

[0074] 818. Graphical User Interface;

[0075] 820, network interface;

[0076] 822, communication link;

[0077] 824. Communication bus. DETAILED DESCRIPTION

[0078] In order to help those skilled in the art better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the embodiments described are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of this specification.

[0079] It should be noted that the terms "first," "second," and the like in the specification and claims herein and in the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or devices.

[0080] This specification provides method operation steps as described in the embodiments or flowcharts, but more or fewer operation steps may be included based on routine or non-creative work. The order of steps listed in the embodiments is only one way of executing the steps among many orderings and does not represent the only execution order. When a system or device product is actually executed, the method can be executed in the order shown in the embodiments or the drawings or in parallel.

[0081] It should be noted that the method herein can be used in the field of transportation, and can also be used in any field other than the field of transportation. The application fields of the method and device herein are not limited.

[0082] Figure 1 The flowchart of a method for rock resistivity spectrum testing based on capillary pressure in an embodiment of this invention is shown, which specifically includes the following steps:

[0083] Step 101: Obtain water-saturated resistivity spectrum data for the core. In some embodiments of this specification, the water-saturated resistivity spectrum data refers to resistivity spectrum data at a water saturation of 100%. The resistivity spectrum data for the core is measured when the external pressure applied to the core is zero and no liquid is displaced from the pores within the core. The water-saturated resistivity spectrum data accurately represents the direction of the capillary pressure curve. Furthermore, the water-saturated resistivity spectrum data for cores or rock samples with different pore structures will vary. In this specification, the water-saturated resistivity spectrum data can be represented as a continuous curve, where the resistivity value changes with frequency. This resistivity change reflects the pore structure of the formation at a microscopic level. Specifically, as the frequency increases, the resistivity of the water-saturated resistivity spectrum gradually decreases. The changes in the water-saturated resistivity spectrum are highly correlated with the pore structure of the core and can reflect the specific morphology of the pore structure at a microscopic level. Therefore, the water-saturated resistivity spectrum data can reflect the pore structure of the core / rock sample to a certain extent.

[0084] Step 102: Input the water-saturated resistivity spectrum data into a pre-built capillary pressure type prediction model to determine the capillary pressure curve type corresponding to the water-saturated resistivity spectrum data. The capillary pressure curve type is represented by the number of characteristic points of the capillary pressure curve and the pressure difference values ​​corresponding to the characteristic points. For example, Figure 7A As shown in the figure, the number of characteristic points of a capillary pressure curve is 7, and the water saturations corresponding to these 7 characteristic points are: 0.45, 0.5, 0.6, 0.8, 0.92, 0.99, and 1 respectively; the pressure difference values ​​corresponding to these 7 characteristic points are 120Pc, 90Pc, 20Pc, 10Pc, 9Pc, 7Pc, and 0Pc respectively. This capillary pressure curve represents a rock sample with a certain pore structure. For example, Figure 7B As shown in the figure, the number of characteristic points of a capillary pressure curve is 5, and the water saturations corresponding to these 5 characteristic points are 0.85, 0.91, 0.94, 0.99, and 1 respectively; the pressure difference values ​​corresponding to these 5 characteristic points are 250Pc, 90Pc, 60Pc, 35Pc, and 0Pc respectively. This capillary pressure curve represents a rock sample with another pore structure.

[0085] In this step, the pre-built capillary pressure type prediction model is trained by the historical water-saturated resistivity spectrum data and the historical capillary pressure curve data corresponding to the historical water-saturated resistivity spectrum data. For details on the training process of the capillary pressure type prediction model, see Figure 2 Detailed description.

[0086] In this specification, the water-saturated resistivity spectrum data obtained in step 101 is used as the input of the capillary pressure type prediction model, and the capillary pressure type prediction model outputs the capillary pressure curve type corresponding to the core corresponding to the water-saturated resistivity spectrum data. The capillary pressure curve type is characterized by the number of characteristic points of the capillary pressure curve and the pressure difference value of these characteristic points. For example, the pore structure of a certain rock sample is relatively loose, and the capillary pressure type predicted by the water-saturated resistivity spectrum data of the rock sample can be represented by only 5 characteristic points; while the pore structure of a certain rock sample is relatively tight, and the capillary pressure type predicted by the water-saturated resistivity spectrum data of the rock sample needs to be represented by 7 characteristic points. Furthermore, each characteristic point in the predicted capillary pressure curve has a corresponding pressure difference value and water inclusion degree.

[0087] Step 103: Determine a pressure differential measurement sequence based on the characteristic points, including a minimum pressure differential value, a maximum pressure differential value, and a pressure differential threshold value. In this step, the pressure differential measurement sequence includes multiple pressure differential measurement points, each of which is a characteristic point determined in step 102 and corresponds to pressure differential value information. The pressure differential measurement sequence includes at least a maximum pressure differential value, a minimum pressure differential value, and a pressure differential threshold value. Furthermore, the pressure differential measurement sequence may also include other pressure differential measurement values.

[0088] Step 104: Measure the resistivity spectrum data of each point in the pressure difference measurement sequence. Figure 5 The rock resistivity spectrum and capillary pressure joint measuring device is used to measure resistivity spectrum data.

[0089] Figure 2 The flowchart of a method for determining a capillary pressure type prediction model according to an embodiment of this invention is shown. This specification obtains a model that can predict the type of capillary pressure curve through training, using a minimum number of feature points to represent the capillary pressure curve, thereby reducing the resistivity spectrum measurement time. Specifically, the steps include:

[0090] Step 201: Obtain historical water-saturated resistivity spectrum data of different cores and historical capillary pressure curve data corresponding to the historical water-saturated resistivity spectrum sample data. In this step, the historical water-saturated resistivity spectrum data of different cores and the historical water-saturated resistivity spectrum data can be data obtained through historical collection or measurement.

[0091] Step 202 : Select a random number of feature points from the historical capillary pressure curve data as initial feature points. The initial feature points do not include the first and last endpoints of the historical capillary pressure curve, and include their corresponding water saturation and pressure difference values.

[0092] In some embodiments of this specification, a random number of feature points are selected from historical capillary pressure curve data as initial feature points, which also serve as labels for training samples of the capillary pressure type prediction model. Three, four, five, six, seven, eight, or even more feature points can be randomly selected from the historical capillary pressure curve data as initial feature points. The randomly selected initial feature points are not the starting points of the historical capillary pressure curve. In this step, the random number of feature points (i.e., initial feature points) constitute a minority of feature points.

[0093] In step 203, the initial feature points and the historical water-saturated resistivity spectrum data are determined as a training sample set. The historical water-saturated resistivity spectrum data is the feature data in the training sample set, and the initial feature points are the labels in the training sample data set. The training sample set can correspond to a rock sample with a certain porosity structure. For example, the five initial feature points in the historical water-saturated resistivity spectrum data and historical capillary pressure data corresponding to rock sample A1 with tight pores serve as the training sample set corresponding to rock sample A1; the seven initial feature points in the historical water-saturated resistivity spectrum data and historical capillary pressure data of rock sample A2 with medium tightness serve as the training sample set for rock sample A2; and the nine initial feature points in the historical water-saturated resistivity spectrum data and historical capillary pressure data corresponding to rock sample A3 with loose pores serve as the training sample set for rock sample A3. In this specification, for rock samples with different pore structures, the initial feature points of the rock samples are used as the training sample set to train the model.

[0094] Step 204: Construct an initial capillary pressure curve based on the training sample set. The initial capillary pressure curve is fitted using the historical water-saturated resistivity spectrum data and a random number of training sample sets. In this step, a binomial fit is used to construct the capillary pressure curve.

[0095] Step 205 calculates the error between the initial capillary pressure curve and the historical capillary pressure curve data. In this step, a quadratic polynomial fit is used to calculate the error between the fitted initial capillary pressure curve and the original historical capillary pressure curve. The error can be a mean relative error, which is the cumulative mean relative error (RMRE), calculated by summing the mean relative errors between each initial feature point in the constructed initial capillary pressure curve and the corresponding points in the historical capillary pressure curve.

[0096] Step 206: Iteratively adjust the number of initial feature points of the initial capillary pressure curve based on the error and the maximum number of iterations. When the error meets a preset error value, determine the capillary pressure type prediction model. In this step, the maximum number of iterations is a pre-set upper limit for the number of iterations of the initial capillary pressure curve, for example, 3, 5, 6, 10, etc. When the cumulative average relative error between each initial feature point in the fitted initial capillary pressure curve and the historical capillary pressure curve meets the error standard, that is, the cumulative average relative error of each initial feature point is less than or equal to the error standard, it can be determined that the currently fitted initial capillary pressure curve is relatively close to the historical capillary pressure curve.

[0097] Figure 3 The flowchart of a method for determining a capillary pressure type prediction model according to an embodiment of the present invention is shown, which specifically includes the following steps:

[0098] Step 301: Determine whether the current number of iterations exceeds the maximum number of iterations. As described in step 206, the maximum number of iterations is a preset upper limit of the number of iterations of the initial capillary pressure curve, which is used to control the degree of model training.

[0099] Step 302: If not, further determine whether the error is greater than a preset error value.

[0100] In step 303, if the error is less than or equal to a preset error value, the minimum number of characteristic points of the initial capillary pressure curve is determined, and the capillary pressure type prediction model corresponding to the minimum number of characteristic points is determined. In this step, for rock samples of the same pore type, different numbers of initial characteristic points can be used to construct the initial capillary pressure curve. For example, for rock samples of the same pore type, the initial capillary pressure curve constructed using 3 initial characteristic points, 5 initial characteristic points, or 7 initial characteristic points will differ. When calculating the error relative to the historical capillary pressure curve using different numbers of initial characteristic points, the accumulated error value will also be different. For example, for rock samples of the same pore type, the error calculated using 3 initial characteristic points is 3%; the error calculated using 5 initial characteristic points relative to the historical capillary pressure curve is 4%; and the error calculated using 7 initial characteristic points relative to the historical capillary pressure curve is 5%. In this step, when the errors between different numbers of initial feature points and historical capillary pressure curves for a rock sample of the same pore type all meet a condition of being less than or equal to a preset error value, the minimum number of initial feature points is selected, and the capillary pressure curve corresponding to the minimum number of initial feature points is determined as the capillary pressure type prediction model. For example, if the preset error value is 5%, and for a rock sample of the same pore type, the cumulative relative evaluation errors between initial capillary pressure curves constructed using 3, 5, or 7 initial feature points and the historical capillary pressure curve are all less than or equal to 5%, the initial capillary pressure curve corresponding to the 3 initial feature points is selected as the capillary pressure type prediction model.

[0101] In step 304, if the error is greater than the preset error value, the number of initial feature points is adjusted, and the steps of iterating the initial capillary pressure curve and determining the error against the preset error value are repeated until the current number of iterations exceeds the maximum number of iterations. If the error is greater than the preset error value, it is considered that the number of initial feature points taken for the rock sample of this pore type does not conform to the curve characteristics, and the number of initial feature points needs to be adjusted to reconstruct the initial capillary pressure curve. Specifically, the initial capillary pressure curve can be constructed by sequentially increasing or decreasing the number of initial feature points until the error condition between the newly constructed initial capillary pressure curve and the original historical capillary pressure curve is met. The number of feature points of the constructed capillary pressure curve is then considered to be the number of points taken.

[0102] In step 305, if yes, the number of initial feature points is adjusted, and the steps of iterating the initial capillary pressure curve and determining the error against the preset error value are re-executed. If the current number of iterations exceeds the maximum number of iterations, it indicates that the current process of iterating the initial capillary pressure curve no longer meets the training standard or training requirements, and it is necessary to re-iterate the capillary pressure curve by adjusting the number of initial feature points.

[0103] In some embodiments of the present specification, adjusting the number of the initial feature points and re-executing the step of determining the error and the preset error value includes: sequentially increasing the number of the initial feature points and calculating the error between the initial capillary pressure curve and the historical capillary pressure curve data; or sequentially decreasing the number of the initial feature points and calculating the error between the initial capillary pressure curve and the historical capillary pressure curve data.

[0104] In some embodiments of the present specification, the historical water-saturated resistivity spectrum data is also pre-processed, including filtering and noise reduction, curve smoothing, and standardization.

[0105] Figure 4 The flowchart of a method for measuring resistivity spectrum data in a differential pressure measurement sequence according to an embodiment of the present invention is shown, which specifically includes the following steps:

[0106] Step 401: Determine a differential pressure measurement sequence based on the characteristic points and the differential pressure values. The differential pressure measurement sequence may be determined based on the water saturation and differential pressure values ​​corresponding to the characteristic points. The differential pressure measurement sequence may include two or more characteristic points.

[0107] Step 402 : When each pressure differential measurement point in the pressure differential measurement sequence meets the pressure differential balance condition, the resistivity spectrum under the pressure differential balance condition is measured. The pressure differential balance condition includes the pressure differential balance time and the balanced liquid output.

[0108] In some embodiments of the present specification, when measuring the resistivity spectrum, it is necessary to ensure that the equilibrium condition is reached at each pressure difference measurement point. The equilibrium condition requires that the difference between the liquid output at the equilibrium detection time and the monitoring time is less than the equilibrium liquid output. Specifically, the pressure difference balance monitoring time, the equilibrium liquid output and the different pressure difference measurement points are set in advance by computer software. When the equilibrium monitoring time and the difference between the liquid output at the monitoring time are less than the equilibrium liquid output under each pressure difference condition, it is considered that the stable point of the pressure difference has been reached. At this time, the total liquid output and the pressure difference value are recorded, and the complex resistivity spectrum of the rock sample under the current conditions is measured, and the measurement is completed. Then measure the next set pressure difference measurement point and repeat the above operation until the experimental measurement of all set pressure difference measurement points is completed. Save the experimental data and generate an experimental measurement report on the correspondence between capillary pressure and complex resistivity spectrum.

[0109] Figure 5 The figure shows a structural schematic diagram of a rock resistivity spectrum and capillary pressure joint measurement device according to an embodiment of this invention, wherein the device includes a core holder, an electrical spectrum measurement system, and a hydraulic system.

[0110] Specifically, the device includes a displacement pump 501, a confining pressure pump 502, a metering pump 503, a displacement pump liquid suction bottle 504, a confining pressure pump liquid suction bottle 505, a metering pump liquid suction bottle 506, a core chamber upper end pressure sensor 507, a core chamber lower end pressure sensor 508, a differential pressure sensor 509, a pipeline control switch 510, a temperature sensor 511, a core chamber lower end pressure relief valve 512, a displacement pump oil storage tank 513, a displacement pump water storage tank 514, a data acquisition board 515, a computer display 516, a computer 517, an impedance analyzer 518, a constant temperature box 519, a pressure pump manual controller 520, a core chamber upper end pressure relief valve 521, a core chamber upper end temperature sensor 522, and a core holder 523. The impedance analyzer 518 is used to measure the complex resistivity spectrum of rock samples. The measurement frequency range is 40 Hz to 110 MHz, and it supports swept frequency measurement. The pressure pump manual controller 520 is used to control the metering pump, confining pressure pump and displacement pump to generate pressure, and the pressure value range is 0-10000psi.

[0111] The core holder 523 comprises upper and lower clamping pistons, a core holding chamber, upper and lower liquid outlet pipes, upper and lower pressure relief valves, and a semi-permeable diaphragm. The upper and lower pistons are connected to two power supply electrodes, and the side ends of the core holding chamber are connected to two measuring electrodes. The core holder 523 is used to clamp the core. In some embodiments of this specification, a confining pressure is applied within the core holder, creating a pressure differential between the upper and lower ends to displace water from the semi-permeable diaphragm. The displaced water volume is calculated, and the resistivity spectrum under this pressure differential is measured using a quadrupole method.

[0112] In some embodiments of this specification, the rock resistivity spectrum and capillary pressure combined measurement device also includes a constant temperature control system, which includes a constant temperature box 519, a temperature sensor, a temperature acquisition board, a temperature measurement circuit, and an electric heater. Temperature data measured by the constant temperature system is connected to a computer 517 via wires.

[0113] In some embodiments of the present specification, the electrical spectrum measurement system includes an impedance analyzer 518, power supply electrodes, and measurement electrodes for measuring resistance spectrum data. The electrodes are connected to the core holder via wires, and the impedance analyzer 518 is connected to the computer 517 via wires.

[0114] In some embodiments of the present specification, the hydraulic system includes an upstream pressure sensor, a downstream pressure sensor, a differential pressure sensor, a metering pump, a displacement pump, a confining pressure pump, and a pump controller. The hydraulic system is used to apply different pressure differentials to the core. The metering pump, displacement pump, and confining pressure pump can all be manually operated by the pump controller, which is connected to the computer 517 via wires. Among them, the upstream pressure sensor, downstream pressure sensor, differential pressure sensor, and temperature measurement circuit are connected to the multi-channel acquisition control board via wires. The multi-channel acquisition control board is installed in a cabinet and connected to the computer 517 via wires. Each system can be controlled by the computer 517. The measured data includes complex resistivity spectrum data from the impedance analyzer, solution volume recording data of the metering pump, displacement pump, and confining pressure pump in the capillary pressure measurement part, pressure data at the upper and lower ends of the core, and temperature data recorded by the temperature control system. These data are further processed by the computer 517.

[0115] Figure 6 Figure 6 shows a cross-sectional schematic diagram of a core holder according to an embodiment of this invention. 601 is the clamping knob at the upper end of the core chamber, 602 is the upper power supply electrode, 603 is the displacement port, 604 is the upper exhaust port, 605 is the core sample, 606 is the confining pressure relief valve, 607 is the semi-permeable partition, 608 is the lower exhaust port, 609 is the metering port, 610 is the lower power supply electrode, 611 is the clamping knob at the lower end of the core chamber, 612 is the rubber sleeve, 613 is the first measurement electrode, 614 is the second measurement electrode, 615 is the confining pressure input port, and 616 is the metal housing of the holder.

[0116] In some embodiments of this specification, before conducting resistivity spectrum data measurement experiments, the metering pump, displacement pump, and confining pressure pump must be cleaned, especially the metering pump, to drain the liquid from it. The pipelines must also be cleaned and any air trapped in them removed. The pump cylinders must be rinsed twice with distilled water and twice more with the saline solution used in the experiment, and then the saline solution used in the experiment must be pumped into the pump cylinders. Furthermore, the displacement pump and the fluid reservoir must be completely purged of air.

[0117] In some embodiments of this specification, during the step of loading the semipermeable separator and core sample, the semipermeable separator must be pressurized and saturated together with the core sample to be tested before use. During the loading process, the semipermeable separator is first loaded into the core holder, ensuring good contact between the semipermeable separator and the electrodes at both ends of the holder. The core is then placed to ensure close contact between the sample end surface and the semipermeable separator and electrodes, and the electrodes at both ends of the holder are tightened.

[0118] In some embodiments of this specification, the gas in the pipeline has been discharged in the aforementioned steps, but there is still a small amount of gas at both ends of the clamp, which needs to be discharged before the experiment. Specifically, it is necessary to apply confining pressure first. There may be a gap between the core sample and the inner wall of the clamp. If the exhaust operation of the ports at both ends of the clamp is directly performed without adding confining pressure, the liquid at both ends may flow along the inner wall of the clamp. After the confining pressure causes the rubber sleeve to deform, the exhaust operation is performed. When exhausting, open the exhaust port valve and set a certain pressure. Bubbles will emerge from the exhaust port, and then liquid will be discharged. Close the exhaust port valve when the liquid is discharged stably without bubbles.

[0119] Figure 7A The capillary pressure curve for a pore structure in an embodiment of this invention is shown in the figure. The abscissa of the capillary pressure curve in the figure represents water saturation (SW), and the ordinate represents pressure difference (also known as differential pressure value) Pc / psi. The capillary pressure curve in the figure has seven characteristic points, each of which reflects the water saturation and differential pressure value at that point. Figure 7B A capillary pressure curve diagram of another pore structure according to an embodiment of the present invention is shown. The abscissa of the capillary pressure curve in the figure is SW, and the ordinate is the pressure difference (also referred to as the pressure difference value). The capillary pressure curve in the figure has five characteristic points, each of which reflects the water saturation and pressure difference value at that point.

[0120] like Figure 8As shown, a computer device provided in an embodiment of the present invention is shown. The computer device 802 may include one or more processors 804, such as one or more central processing units (CPUs), each of which may implement one or more hardware threads. The computer device 802 may also include any memory 806 for storing any type of information, such as code, settings, data, etc. For example, without limitation, the memory 806 may include any one or more combinations of the following: any type of RAM, any type of ROM, a flash memory device, a hard disk, an optical disk, etc. More generally, any memory may use any technology to store information. Furthermore, any memory may provide volatile or non-volatile retention of information. Furthermore, any memory may represent a fixed or removable component of the computer device 802. In one embodiment, when the processor 804 executes associated instructions stored in any memory or combination of memories, the computer device 802 may perform any operation of the associated instructions. The computer device 802 also includes one or more drive mechanisms 808 for interacting with any memory, such as a hard disk drive mechanism, an optical disk drive mechanism, etc.

[0121] The computer device 802 may also include an input / output module 810 (I / O) for receiving various inputs (via input devices 812) and for providing various outputs (via output devices 814). A specific output mechanism may include a presentation device 816 and an associated graphical user interface (GUI) 818. In other embodiments, the input / output module 810 (I / O), input devices 812, and output devices 814 may not be included, and the computer device 802 may simply be a computer device in a network. The computer device 802 may also include one or more network interfaces 820 for exchanging data with other devices via one or more communication links 822. One or more communication buses 824 couple the components described above together.

[0122] The communication link 822 may be implemented in any manner, for example, via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication link 822 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0123] Corresponding to Figures 1 to 4 The embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which executes the steps of the above method when executed by a processor.

[0124] The embodiment of the present invention also provides a computer readable instruction, wherein when the processor executes the instruction, the program causes the processor to execute the following Figures 1 to 4The method shown.

[0125] It should be understood that in the various embodiments of this document, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this document.

[0126] It should also be understood that in the embodiments herein, the term "and / or" merely describes an association between associated objects, indicating that three possible relationships exist. For example, "A and / or B" could represent: A alone, A and B simultaneously, or B alone. Furthermore, the character " / " in this document generally indicates an "or" relationship between the associated objects.

[0127] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this document.

[0128] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0129] In the several embodiments provided herein, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices, or units, or can be an electrical, mechanical, or other form of connection.

[0130] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments herein.

[0131] In addition, the functional units in the various embodiments herein may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0132] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this article is essentially or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of this article. The aforementioned storage medium includes: various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0133] This article uses specific embodiments to illustrate the principles and implementation methods of this article. The description of the above embodiments is only used to help understand the methods and core ideas of this article. At the same time, for those skilled in the art, based on the ideas of this article, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation to this article.

Claims

1. A rock resistivity spectrum testing method based on capillary pressure, characterized in that: The method comprises: Obtain water-saturated resistivity spectrum data of the core; Inputting the water-saturated resistivity spectrum data into a pre-built capillary pressure type prediction model to determine the capillary pressure curve type corresponding to the water-saturated resistivity spectrum data, wherein the capillary pressure curve type is represented by the number of characteristic points of the capillary pressure curve and the pressure difference values ​​corresponding to the characteristic points; Determining a pressure difference measurement sequence according to the characteristic points, including a minimum pressure difference value, a maximum pressure difference value, and a pressure difference threshold value; Measure resistivity spectrum data at each point in the differential pressure measurement sequence.

2. The rock resistivity spectrum testing method based on capillary pressure according to claim 1 is characterized in that: The training process of the capillary pressure type prediction model is as follows: Obtaining historical water-saturated resistivity spectrum data of different cores and historical capillary pressure curve data corresponding to the historical water-saturated resistivity spectrum data; Selecting a random number of feature points from the historical capillary pressure curve data as initial feature points, wherein the initial feature points do not include the first and last endpoints of the historical capillary pressure curve, and the initial feature points include their corresponding water saturation and pressure difference values; Determine the initial feature points and the historical water-saturated resistivity spectrum data as a training sample set; constructing an initial capillary pressure curve according to the training sample set; Calculating the error between the initial capillary pressure curve and historical capillary pressure curve data; According to the error and the maximum number of iterations, the number of initial characteristic points of the initial capillary pressure curve is iteratively adjusted, and when the error meets a preset error value, a capillary pressure type prediction model is determined.

3. The rock resistivity spectrum testing method based on capillary pressure according to claim 2 is characterized in that: Iteratively adjusting the number of initial characteristic points of the initial capillary pressure curve according to the error and the maximum number of iterations to determine the capillary pressure type prediction model includes: Determine whether the current number of iterations exceeds the maximum number of iterations; If not, further determining whether the error is greater than a preset error value; If the error is less than or equal to a preset error value, determining the minimum number of characteristic points of the initial capillary pressure curve, and determining a capillary pressure type prediction model corresponding to the minimum number of characteristic points; If the error is greater than the preset error value, adjusting the number of the initial feature points, and re-performing the steps of iterating the initial capillary pressure curve and determining the error and the preset error value until the current number of iterations exceeds the maximum number of iterations; If so, the number of the initial feature points is adjusted, and the steps of iterating the initial capillary pressure curve and determining the error and the preset error value are re-executed.

4. The rock resistivity spectrum testing method based on capillary pressure according to claim 3 is characterized in that: The step of adjusting the number of the initial feature points and re-judging the error and the preset error value comprises: Increasing the number of initial characteristic points sequentially, and calculating the error between the initial capillary pressure curve and historical capillary pressure curve data; or The number of initial characteristic points is reduced sequentially, and the error between the initial capillary pressure curve and historical capillary pressure curve data is calculated.

5. The rock resistivity spectrum testing method based on capillary pressure according to claim 1 is characterized in that: The method further comprises: determining a pressure difference measurement sequence according to the characteristic points and the pressure difference values; When each pressure difference measurement point in the pressure difference measurement sequence meets the pressure difference balance condition, the resistivity spectrum under the pressure difference balance condition is measured. The pressure difference balance condition includes the pressure difference balance time and the balanced liquid output.

6. The rock resistivity spectrum testing method based on capillary pressure according to claim 5 is characterized in that: The method further includes: preprocessing the historical water-saturated resistivity spectrum data, including: filtering and noise reduction, curve smoothing, and standardization.

7. A device for combined measurement of rock resistivity spectrum and capillary pressure, comprising a core holder, an electrical spectrum measurement system, and a hydraulic system, characterized in that: The core clamp includes upper and lower clamping pistons, a core clamping chamber, upper and lower liquid outlet pipes, upper and lower pressure relief valves, and a semi-permeable partition. The upper and lower pistons are connected to two power supply electrodes, and the side ends of the core clamping chamber are connected to two measuring electrodes. The core clamp is used to clamp the core; The electrical spectrum measurement system includes an impedance analyzer, a power supply electrode and a measurement electrode, and is used to measure resistivity spectrum data; The hydraulic system includes an upstream pressure sensor, a downstream pressure sensor, a differential pressure sensor, a metering pump, a displacement pump, a confining pressure pump and a pump controller, and is used to apply pressure differences of different sizes to the core.

8. The rock resistivity spectrum and capillary pressure joint measurement device according to claim 7, characterized in that: The electrical spectrum measurement system is further used to measure resistivity spectrum data of each characteristic point based on the number of characteristic points predicted and output by the capillary pressure type prediction model and the pressure difference values ​​corresponding to the characteristic points.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

Images