Ternary correlation modeling method for synergistic effect of reservoir minerals and major elements
By employing a ternary correlation modeling method based on the synergistic effect of reservoir minerals and major elements, the accuracy problem in analyzing reservoir state changes after chemical flooding was solved. This method enabled accurate assessment of secondary amorphous gels and quantification of permeability changes, thereby improving oil recovery.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- DAQING OILFIELD CO LTD
- Filing Date
- 2026-02-24
- Publication Date
- 2026-05-12
AI Technical Summary
Existing technologies struggle to accurately analyze changes in reservoir condition after chemical flooding, particularly the distribution and blockage of secondary amorphous gels, leading to decreased reservoir permeability and reduced recovery.
A ternary correlation modeling method based on the synergistic effect of reservoir minerals and major elements was adopted. By obtaining the elemental abundance and mineral source abundance of initial and segmented core samples, the system bias was corrected by the determination correction factor, the residual abundance of secondary amorphous gel was evaluated, and the ternary correlation evaluation was carried out in combination with permeability resistance, mineral dissolution and seepage improvement parameters.
It improves the accuracy of evaluating reservoir state changes, can identify and quantify the formation and distribution of secondary amorphous gels, accurately assess reservoir permeability changes, and improve recovery rate.
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Figure CN122016598A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of reservoir analysis technology, specifically to a ternary correlation modeling method for the synergistic effect of reservoir minerals and major elements. Background Technology
[0002] In the field of enhanced oil recovery (EOR) through chemical flooding in petroleum engineering, ASP (asphalt-asphalt) flooding is a mature and crucial technology. It involves injecting chemicals containing strong alkalis, surfactants, and polymers into the subsurface reservoir to reduce oil-water interfacial tension and improve oil washing efficiency. However, a strongly alkaline environment can induce non-isomorphic dissolution reactions in the reservoir's framework minerals (such as feldspar, quartz, and clay minerals), readily generating secondary amorphous gels (such as hydrated aluminosilicates or silica gels) at the pore throats. This leads to a decrease in reservoir permeability and reduced EOR; therefore, analyzing the changes in reservoir state after chemical flooding is essential.
[0003] Conventional X-ray diffraction (XRD) techniques rely on lattice diffraction peaks and cannot identify secondary amorphous gels lacking characteristic diffraction peaks. This makes it difficult to reveal the contradictory phenomenon of significant mineral dissolution (theoretically increasing pore size) but a sharp decrease in permeability (actually due to blockage). Furthermore, the heterogeneity of reservoirs causes chromatographic separation of chemical agents during propulsion, resulting in uneven spatial distribution of secondary amorphous gels within the reservoir. Existing averaging evaluation methods ignore this spatial distribution difference, easily overlooking high-resistance risks such as end effects or throat narrowing, leading to low accuracy in analyzing reservoir state changes. Summary of the Invention
[0004] To address the low accuracy of existing technologies for analyzing reservoir state changes, this invention aims to provide a ternary correlation modeling method for the synergistic effect of reservoir minerals and major elements. The specific technical solution adopted is as follows:
[0005] Initial core samples were extracted from the reservoir core to obtain the pre-displacement permeability of the remaining reservoir core; after chemical displacement experiments were conducted on the remaining reservoir core, the post-displacement permeability was obtained, and the core samples were segmented and sampled to obtain segmented core samples; for each core sample, the elemental abundance and mineral source abundance of the preset major elements were obtained.
[0006] Based on the deviation of the elemental abundance of the preset major elements in the initial core sample relative to the mineral source abundance, a measurement correction factor is obtained. Combined with the elemental abundance of the preset major elements and the mineral source abundance in each segment of the core sample, the residual abundance of secondary amorphous gel in each segment of the core sample is obtained.
[0007] The permeability resistance parameters were obtained based on the residual abundance of secondary amorphous gel in each segmented core sample; the mineral dissolution parameters were obtained based on the difference between the mineral source abundance of the preset major elements in the initial core sample and the segmented core samples; and the seepage improvement parameters were determined based on the pre-drainage permeability and post-drainage permeability.
[0008] Ternary correlation reservoir evaluation is based on permeability resistance parameters, mineral dissolution parameters, and seepage improvement parameters.
[0009] Furthermore, the method for obtaining the elemental abundance and the mineral source abundance includes:
[0010] For each core sample, the contents of preset major elements, preset inert elements, and the mineral content of crystalline minerals containing preset major elements are determined. The preset inert elements do not include the preset major elements. Based on the mineral content of each crystalline mineral and the mass fraction of the preset major elements within it, the mineral source content of the preset major elements is determined.
[0011] The element abundance of the preset major element is determined based on the ratio of the preset major element content to the preset inert element content; the mineral source abundance of the preset major element is determined based on the ratio of the mineral source content of the preset major element to the preset inert element content.
[0012] Furthermore, the method for obtaining the determination correction factor includes:
[0013] The ratio of the abundance of the preset major elements in the initial core sample to the abundance of the mineral source is used as the determination correction factor.
[0014] Furthermore, the method for obtaining the residual abundance includes:
[0015] For each segment of core sample, the mineral source abundance of the preset major elements is weighted using the determination correction factor, and the residual abundance of the secondary amorphous gel is determined based on the deviation of the weighted result from the elemental abundance of the preset major elements.
[0016] Furthermore, the method for obtaining the permeation resistance parameter includes:
[0017] Distribution and aggregation parameters were obtained based on the distribution characteristics of the residual abundance of secondary amorphous gel in each segmented core sample.
[0018] The residual blockage parameters were obtained based on the cumulative characteristics of the residual abundance of secondary amorphous gel in all segmented core samples.
[0019] By combining the distribution aggregation parameters and the residual blockage parameters, the permeability resistance parameters of the remaining reservoir core are obtained.
[0020] Furthermore, the method for obtaining the distribution aggregation parameters includes:
[0021] The uniform distribution probability is determined based on the proportion of a single segmented core sample in the remaining reservoir cores; the actual distribution probability is determined based on the proportion of the residual abundance of each segmented core sample in the sum of the residual abundance of all segmented core samples; and the distribution aggregation parameters are determined based on the difference between the uniform distribution probability and the actual distribution probability.
[0022] Furthermore, the method for obtaining the mineral dissolution parameters includes:
[0023] Based on the average characteristics of the mineral source abundance of the preset major elements in all segmented core samples, the post-displacement mineral source abundance of the preset major elements after the chemical displacement experiment is determined; based on the difference between the mineral source abundance of the preset major elements in the initial core samples and the post-displacement mineral source abundance, mineral dissolution parameters are obtained.
[0024] Furthermore, the method for obtaining the seepage improvement parameters includes:
[0025] Based on the ratio between the pre-drainage permeability and the post-drainage permeability, seepage improvement parameters are obtained.
[0026] Furthermore, a ternary correlation reservoir evaluation is conducted based on permeability resistance parameters, mineral dissolution parameters, and seepage improvement parameters, including:
[0027] A ternary correlation evaluation model is constructed based on the aforementioned permeability resistance parameters, mineral dissolution parameters, and seepage improvement parameters:
[0028] When the seepage improvement parameter is less than the preset improvement threshold, the reservoir is determined to be blocked; wherein, if the permeability resistance parameter is greater than the preset resistance threshold, the reservoir is determined to be subject to precipitation blockage; if the permeability resistance parameter is less than or equal to the preset resistance threshold, the reservoir is determined to be subject to non-precipitation blockage.
[0029] When the seepage improvement parameter is greater than the preset improvement threshold and the mineral dissolution parameter is greater than the preset dissolution threshold, reservoir dissolution is determined.
[0030] Except for reservoir blockage and reservoir dissolution, the reservoir is considered to be in an inert or dissolution-precipitation equilibrium state.
[0031] Furthermore, segmented sampling to obtain segmented core samples includes:
[0032] After the chemical displacement experiment, core samples of uniform length were cut from the beginning, end and middle of the remaining reservoir core.
[0033] The present invention has the following beneficial effects:
[0034] This invention involves extracting initial core samples from reservoir cores to obtain the pre-displacement permeability of the remaining reservoir cores; conducting chemical displacement experiments on the remaining reservoir cores to obtain post-displacement permeability, and then segmenting and sampling to obtain segmented core samples; based on different measurement systems, obtaining the elemental abundance and mineral origin abundance of preset major elements in each core sample; then assessing the systematic bias of different element measurement systems based on the deviation of the elemental abundance of preset major elements relative to the mineral origin abundance in the initial core samples, obtaining measurement correction factors to prepare for subsequent compensation of systematic bias, and further combining the elemental abundance and mineral origin abundance of preset major elements in each segmented core sample... To accurately analyze the abundance gap and assess the formation of secondary amorphous gel, the residual abundance of secondary amorphous gel in each segmented core sample is obtained, thereby accurately acquiring permeability resistance parameters. Then, based on the difference in the mineral source abundance of the preset major elements between the initial core sample and the segmented core samples, the changes in crystalline mineral content in the core samples before and after chemical flooding are compared to obtain mineral dissolution parameters. Furthermore, the seepage improvement parameters are determined based on the relative changes in permeability before and after flooding. Finally, a ternary correlation reservoir evaluation is performed based on the permeability resistance parameters, mineral dissolution parameters, and seepage improvement parameters to improve the accuracy of the evaluation of reservoir state changes. Attached Figure Description
[0035] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0036] Figure 1 This is a flowchart of a ternary correlation modeling method for the synergistic effect of reservoir minerals and major elements, provided as an embodiment of the present invention. Detailed Implementation
[0037] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a ternary correlation modeling method for the synergistic effect of reservoir minerals and major elements proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.
[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.
[0039] The following description, in conjunction with the accompanying drawings, details the specific scheme of the ternary correlation modeling method for the synergistic effect of reservoir minerals and major elements provided by this invention.
[0040] Please see Figure 1 The diagram illustrates a flowchart of a ternary correlation modeling method for the synergistic effect of reservoir minerals and major elements according to an embodiment of the present invention, specifically including:
[0041] Step S1: Extract initial core samples from the reservoir core to obtain the pre-displacement permeability of the remaining reservoir core; after conducting chemical displacement experiments on the remaining reservoir core, obtain the post-displacement permeability, and then cut and sample to obtain segmented core samples; for each core sample, obtain the elemental abundance and mineral source abundance of the preset major elements.
[0042] To analyze and quantify the dissolution / precipitation of mineral reservoirs before and after chemical flooding, one embodiment of the present invention first obtains reservoir cores. Before the chemical flooding experiment, a 1 cm thick slice is cut from the entrance section of the reservoir core as an initial core sample S0 that has not been eroded by the chemical agent. The initial core sample is used as the original background reference for subsequent determination of mineral elements.
[0043] Then, the pre-flood permeability of the remaining reservoir cores after the initial core samples were obtained, providing an initial comparative basis for assessing the changes in reservoir permeability before and after chemical flooding; specifically, the measurement was performed using a steady-state method, a well-known technique, which is briefly described here:
[0044] Before the chemical displacement experiment begins, the remaining reservoir cores are loaded into the core holder, a high-pressure constant-speed horizontal flow pump is connected to the inlet of the core holder, a back pressure valve and a precision pressure sensor are connected to the outlet, and an automatic data acquisition system is connected; simulated formation water is prepared with a salinity consistent with that of the target reservoir formation water; the simulated formation water is vacuum degassed to prevent bubble interference.
[0045] Apply confining pressure to the formation pressure condition (e.g., 20 MPa), and inject simulated formation water into the remaining reservoir core at a constant flow rate (e.g., 0.1-0.5 mL / min). After the pressure difference between the two ends of the core stabilizes (stable fluid flows out of the outlet end of the remaining reservoir core, and the fluctuation of the inlet and outlet pressure difference is less than 1%, indicating that it is saturated and the flow has reached a steady state); in order to eliminate random errors under a single flow rate, different injection flow rates Qi (e.g., 0.1, 0.2, 0.5 mL / min) are set.
[0046] For each injection flow rate, record the pressure difference ΔP and injection flow rate Q across the remaining reservoir core after steady state; calculate the liquid permeability according to Darcy's law. (Unit: mD); where, Injection flow rate (units converted to cm³ / s). To simulate the viscosity of formation water (cP or mPa·s), L is the length of the remaining reservoir core (cm), A is the cross-sectional area of the remaining reservoir core (cm²), and ΔP is the pressure difference (Pa) between the two ends of the remaining reservoir core after steady state. Then, the fluid permeability calculated under different injection flow rates is... Take characteristic values such as the mean or median as the pre-expansion penetration rate.
[0047] Further chemical displacement experiments (such as strong alkali ternary composite flooding) were conducted on the remaining reservoir cores. The techniques are well-known and will not be described in detail here. After the chemical displacement experiments, the above steady-state measurement method was repeated to obtain the post-flooding permeability of the remaining reservoir cores. The Shenyu reservoir cores were then segmented and sampled to obtain segmented core samples. Specifically, physical cutting and partitioning were performed along the fluid flow direction to obtain N (N≥2) segmented core samples.
[0048] Preferably, in one embodiment of the present invention, segmented sampling to obtain segmented core samples includes:
[0049] After the chemical displacement experiment, core samples of uniform length were cut from the beginning, end and middle of the remaining reservoir core.
[0050] As an example, N is 3; a 1cm thin rock section is cut from the first end (flow inlet end face) of the remaining reservoir core and recorded as segmented core sample S1; a 1cm thin rock section is cut from the middle section of the remaining reservoir core and recorded as segmented core sample S2; a 1cm thin rock section is cut from the tail end (flow outlet end face) of the remaining reservoir core and recorded as segmented core sample S3.
[0051] In other examples, implementers can also adjust the number of cuts, the length of cuts, and the position of cuts according to the actual situation.
[0052] After obtaining all core samples (including the initial core sample and all segmented core samples), further pretreatment can be carried out to prepare for subsequent measurements. Specifically, each core sample is washed with oil to remove residual crude oil and organic chemicals from the pores, and then dried to constant weight. The core sample is then ground into powder with a particle size of less than 200 mesh (74 micrometers) using an agate mortar or ball mill. The ground powder is then thoroughly mixed to ensure the representativeness of the sample and to meet the injection requirements for subsequent analysis.
[0053] Further determination of the elemental abundance and mineral origin abundance of the preset major elements in each core sample; the elemental abundance is determined directly based on the elemental angle of the preset major elements in the core sample, while the mineral origin abundance is determined based on the crystalline mineral angle of the core sample. Since the secondary amorphous gel cannot be determined as a crystalline mineral, the elemental content of the preset major elements in it cannot be determined. This can be used to prepare for subsequent comparison of elemental content gaps and assessment of the content of secondary amorphous gel to evaluate the reservoir.
[0054] Preferably, in one embodiment of the present invention, considering that the elemental content of a preset major element can characterize its abundance in segmented core samples, but since chemical flooding may cause dissolution of the core sample, resulting in a reduction in the total mass of the core sample, directly using mass percentage to represent elemental content may lead to an inflated elemental content, thereby interfering with subsequent evaluation results; while inert elements (chemically stable, do not react with strong alkalis and do not migrate with fluids) in the core sample, the content of inert elements can provide an absolute benchmark reference, thereby determining the true relative abundance of the preset major element; therefore, the method for obtaining elemental abundance and mineral source abundance includes:
[0055] For each core sample, the contents of the preset major elements, preset inert elements, and the mineral content of crystalline minerals containing the preset major elements are determined. The preset inert elements do not include the preset major elements. Based on the mineral content of each crystalline mineral and the mass fraction of the preset major elements within it, the mineral source content of the preset major elements is determined.
[0056] The element abundance of the preset major elements is determined based on the ratio of the preset major element content to the preset inert element content; the mineral source abundance of the preset major elements is determined based on the ratio of the mineral source content of the preset major elements to the preset inert element content.
[0057] As an example, taking any core sample as an example, the preset major elements are the elements with high abundance in the reservoir core, and the preset inert elements do not include the preset major elements; the preset major elements are silicon (Si), but the implementer can also adjust them to other elements or combinations of elements such as aluminum (Al) according to the actual situation; the preset inert elements are one or more combinations of titanium (Ti), zirconium (Zr), yttrium (Y), etc.
[0058] Whole-rock analysis was performed using X-ray fluorescence spectrometry (XRF) to determine the contents of preset major elements (W1) and preset inert elements (W0) in the core samples (a well-known technique, which will not be described in detail here). When the preset inert element is a combination of multiple elements, the contents of the multiple elements are averaged to obtain the preset inert element content.
[0059] X-ray diffraction (XRD) was used to detect crystals and determine the mineral content of crystalline minerals containing a predetermined major element (such as potassium feldspar, sodium feldspar, quartz, and kaolinite) (a well-known technique, which will not be elaborated further). For each crystalline mineral, the mass fraction of the predetermined major element, silicon (Si), was determined (a common fact, determined by calculation using chemical formulas, such as the mass fraction of Si in quartz SiO2 being 0.467). The mass fraction was multiplied by the mineral content of the crystalline mineral to obtain the mineral source content W11 of the predetermined major element in the crystalline mineral.
[0060] Furthermore, by using the preset principal element content as the numerator, and to avoid overflow in the calculation results due to the extremely small preset inert element content, a trace stability factor can be added to the preset inert element content, such as... The ratio is then used as the denominator to determine the abundance of the preset principal elements. By introducing an absolute benchmark denominator, interference bias caused by the dissolution of core samples can be avoided, and the relative abundance of the preset principal elements can be accurately measured.
[0061] Similarly, the sum of the mineral source contents of the preset major elements in each crystalline mineral is used as the numerator, and the preset inert element contents are added to the trace stability factor, such as... The ratio is then used as the denominator to determine the mineral source abundance of the predefined major element.
[0062] Step S2: Based on the deviation between the elemental abundance of the preset major elements in the initial core sample and the mineral source abundance, obtain the measurement correction factor, and combine the elemental abundance of the preset major elements and the mineral source abundance in each segment of the core sample to obtain the residual abundance of secondary amorphous gel in each segment of the core sample.
[0063] Since XRF (whole rock analysis) and XRD (crystal analysis) are two instruments with completely different physical principles, even when measuring the same core sample, the results of element content or abundance measurements given by the two instruments may have inherent systematic biases (such as differences in instrument sensitivity, standard sample fitting errors, etc.); directly comparing element abundance with mineral origin abundance to assess the content of secondary amorphous gel may introduce systematic errors.
[0064] To eliminate systematic errors caused by different measurement systems, the correction factor can be determined by using the original background reference provided by the initial core sample. Since the initial core sample has not been eroded by chemical agents, there is no secondary amorphous gel inside, thus providing a relatively clean reference standard.
[0065] Based on this, in a preferred embodiment of the present invention, the ratio of the elemental abundance of a preset major element to the mineral source abundance in the initial core sample is used as a determination correction factor.
[0066] The determination of the correction factor characterizes the systematic proportional relationship between whole-rock detection and crystal detection under the absence of chemical erosion interference, which prepares for subsequent correction of systematic errors and accurate assessment of the content of secondary amorphous gel before and after chemical flooding.
[0067] After obtaining the determination correction factor, the elemental abundance and mineral source abundance of the preset major elements in each segment core sample can be combined to obtain the residual abundance of secondary amorphous gel in each segment core sample. The residual abundance of secondary amorphous gel can help assess the risk of secondary precipitation after chemical flooding blocking the segment core sample, and prepare for subsequent reservoir evaluation.
[0068] Preferably, in one embodiment of the present invention, considering the systematic proportional relationship corresponding to the determination correction factor, the determination results of different determination systems can be converted into the same determination system for comparison, thereby accurately assessing the content or abundance gap of the preset major element, and thus indirectly assessing the residual abundance of the secondary amorphous gel; therefore, the method for obtaining the residual abundance includes:
[0069] For each segment of core sample, the mineral source abundance of the preset major elements is weighted using a determination correction factor, and the residual abundance of the secondary amorphous gel is determined based on the deviation of the weighted result from the elemental abundance of the preset major elements.
[0070] As an example, taking any segment of core sample as an example, the determination correction factor is multiplied by the mineral source abundance of the preset major element, and the product is further subtracted from the elemental abundance of the preset major element to obtain the residual abundance of the secondary amorphous gel.
[0071] The product (weighted result) can characterize the mineral source abundance of the preset major element after systematic error correction, which originates only from crystalline minerals; while the elemental abundance of the preset major element originates from crystalline minerals and secondary amorphous gels; by subtraction, the abundance gap caused by secondary amorphous gels can be evaluated, and thus the residual abundance of secondary amorphous gels can be obtained.
[0072] It should be noted that, in order to prevent the calculation results from having a negative value that has no physical meaning due to random measurement errors, when the difference between the element abundance of the preset principal element and the product is less than 0, the negative value is truncated, and the residual abundance of the secondary amorphous gel is directly set to 0.
[0073] Step S3: Obtain permeability resistance parameters based on the residual abundance of secondary amorphous gel in each segmented core sample; obtain mineral dissolution parameters based on the difference in mineral source abundance of preset major elements between the initial core sample and the segmented core samples; and determine seepage improvement parameters based on pre-drainage permeability and post-drainage permeability.
[0074] After obtaining the residual abundance of secondary amorphous gel in each segment of core sample, the risk of secondary amorphous gel blocking reservoir pores can be further comprehensively assessed, thereby evaluating the reservoir's permeability resistance parameters. The permeability resistance parameters quantify the reservoir's blocking effect of secondary amorphous gel on seepage channels, providing an evaluation basis for subsequent reservoir modeling and evaluation.
[0075] Considering that directly averaging the residual abundance of secondary amorphous gel in all segmented core samples can easily overlook high resistance risks such as end effects or throat narrowing, thus affecting the accuracy of reservoir evaluation; however, based on the distribution characteristics of the residual abundance of secondary amorphous gel in segmented core samples cut from different parts of the remaining reservoir core, it can help assess the risk of uneven distribution of secondary amorphous gel in the reservoir, thereby avoiding the masking of local blockage risks; furthermore, by comprehensively assessing the overall blockage risk by integrating the cumulative characteristics of the residual abundance of secondary amorphous gel in all segmented core samples, the accuracy of reservoir seepage resistance parameter assessment can be improved.
[0076] Based on this, in a preferred embodiment of the present invention, the method for obtaining the permeation resistance parameter includes:
[0077] Distribution and aggregation parameters are obtained based on the distribution characteristics of the residual abundance of secondary amorphous gel in each segmented core sample; residual blocking parameters are obtained based on the cumulative characteristics of the residual abundance of secondary amorphous gel in all segmented core samples; and the distribution and aggregation parameters and residual blocking parameters are combined to obtain the permeability resistance parameters of the remaining reservoir core.
[0078] In a preferred embodiment of the present invention, considering the assumption of uniform distribution in the secondary amorphous gel, a uniform distribution probability can be initially determined; the proportion of the residual abundance of each segment of the core sample relative to the total residual abundance can help assess the actual distribution probability, and the actual distribution probability can help assess the relative residual aggregation at a certain segment of the core sample; furthermore, the relative entropy can be calculated to assess the distribution concentration of the secondary amorphous gel. The larger the relative entropy, the greater the deviation from uniform distribution, and the higher the risk of local aggregation; therefore, the method for obtaining the distribution aggregation parameters includes:
[0079] The uniform distribution probability is determined based on the proportion of a single segmented core sample in the remaining reservoir cores; the actual distribution probability is determined based on the proportion of the residual abundance of each segmented core sample in the sum of the residual abundance of all segmented core samples; and the distribution aggregation parameters are determined based on the difference between the uniform distribution probability and the actual distribution probability.
[0080] As an example, the thickness of a single segmented core sample (1 cm in this example) is divided by the total length of the remaining reservoir cores to assess the segmentation ratio, yielding the uniform distribution probability Q. The sum of the residual abundance of all segmented core samples is calculated, and the residual abundance of each segmented core sample is divided by the sum to obtain the actual distribution probability P. The difference between the actual distribution probability P and the uniform distribution probability Q is further calculated using the relative entropy calculation formula to obtain the relative entropy (a well-known technique, which will not be elaborated further). The relative entropy is then normalized, for example, by mapping it to the sigmoid function to adjust the value range, to obtain the distribution clustering parameter.
[0081] It should be noted that when the sum of the residual abundance of all segmented core samples is 0, the distribution aggregation parameters are not calculated or subsequent modeling and evaluation are not performed. Instead, the reservoir is directly evaluated as not having experienced precipitation blockage after chemical flooding. When the residual abundance of a certain segmented core sample is 0, the calculated actual distribution probability P is also 0. Therefore, its contribution to the relative entropy is 0, and it does not participate in the calculation of the relative entropy to avoid meaninglessness.
[0082] Further calculations were performed to sum and normalize the residual abundance of secondary amorphous gel in all segmented core samples. The dimensions were eliminated and the value range was adjusted. The normalized result was used as a residual blockage parameter to characterize the relative cumulative generation rate of secondary amorphous gel, which comprehensively reflects the cumulative blockage risk. The specific normalization method was to divide the sum by the sum of the elemental abundance of the preset major elements in all segmented core samples.
[0083] The residual blockage parameter is then multiplied by the distribution aggregation parameter to obtain the permeability resistance parameter of the remaining reservoir core; in other examples, implementers may also use other fusion methods such as weighted summation.
[0084] In chemical flooding systems, the reaction between chemical agents (especially strong alkalis with high pH values) and reservoir rocks is essentially a dynamic equilibrium process of dissolution and precipitation: the alkali solution dissolves the rock framework minerals, leading to an increase in pore volume, which theoretically increases permeability (dissolution effect); while the dissolved ions (Si, Al) generate secondary amorphous gels due to environmental changes during migration, which may block pore throats, leading to a decrease in permeability (blocking effect). Therefore, analyzing the dissolution effect of the reservoir can help assess changes in reservoir permeability, and comparing the changes in crystalline mineral content in core samples before and after chemical flooding can help assess the amount of dissolution.
[0085] Therefore, in this embodiment of the invention, mineral dissolution parameters are obtained based on the difference between the mineral source abundance of preset major elements in the initial core sample and the segmented core sample. Mineral dissolution parameters characterize the degree of dissolution of reservoir minerals in a strongly alkaline environment, reflect the permeability enhancement capacity of the reservoir after chemical flooding, and provide an evaluation basis for subsequent reservoir modeling and evaluation.
[0086] Preferably, in one embodiment of the present invention, considering that the mineral source abundance of the preset major elements in the initial core sample can characterize the original mineral quantity, and the mineral source abundance of the preset major elements in all segmented core samples can characterize the remaining undissolved mineral quantity, the amount of dissolved minerals can be assessed by comparing the deviations, thereby assessing the mineral dissolution parameters; therefore, the method for obtaining the mineral dissolution parameters includes:
[0087] Based on the average characteristics of the mineral source abundance of the preset major elements in all segmented core samples, the post-displacement mineral source abundance of the preset major elements after the chemical displacement experiment is determined; based on the difference between the mineral source abundance of the preset major elements in the initial core samples and the post-displacement mineral source abundance, mineral dissolution parameters are obtained.
[0088] As an example, the mineral source abundance of the preset major elements in all segmented core samples is averaged to obtain the post-displacement mineral source abundance of the preset major elements. The difference between the mineral source abundance of the preset major elements in the initial core samples and the post-displacement mineral source abundance is truncated to a negative value, and the difference is divided by the mineral source abundance of the preset major elements in the initial core samples to assess the relative proportion of dissolved minerals, thereby achieving the purpose of normalization and adjusting the value range to obtain mineral dissolution parameters.
[0089] Further, the permeability before and after chemical flooding was used to determine the seepage improvement parameters. The seepage improvement parameters characterize the final macroscopic seepage capacity of the reservoir under the combined effects of dissolution and precipitation, reflect the improvement of the reservoir's seepage capacity before and after chemical flooding, and can also provide an evaluation basis for subsequent reservoir modeling and evaluation.
[0090] Preferably, in one embodiment of the present invention, the seepage improvement parameters are obtained based on the ratio between pre-drainage permeability and post-drainage permeability.
[0091] Specifically, the post-expansion permeability is increased by a small offset, such as... As a molecule, the pre-expander permeability is increased by a small offset, such as... The ratio is then used as the denominator and taken as the seepage improvement parameter K. A slight offset is added to avoid the numerator and denominator being zero, which would render the analysis meaningless. When K is much greater than 1, it indicates that the reservoir permeability has increased and the physical properties have been improved (the dissolution effect is dominant). When K is much less than 1, it indicates that the reservoir permeability has decreased (the blocking effect is dominant). When K is close to 1, it indicates that the reservoir permeability has hardly changed, and the dissolution effect and the blocking effect may have reached a dynamic equilibrium.
[0092] Step S4: Evaluate the ternary correlation reservoir based on permeability resistance parameters, mineral dissolution parameters, and seepage improvement parameters.
[0093] After obtaining the permeability resistance parameters, mineral dissolution parameters, and seepage improvement parameters, a ternary correlation evaluation can be further conducted.
[0094] Preferably, in one embodiment of the present invention, ternary correlation reservoir evaluation is performed based on permeability resistance parameters, mineral dissolution parameters, and seepage improvement parameters, including:
[0095] A ternary correlation evaluation model was constructed based on permeability resistance parameters, mineral dissolution parameters, and seepage improvement parameters.
[0096] When the seepage improvement parameter is less than the preset improvement threshold, the reservoir is determined to be blocked; if the permeability resistance parameter is greater than the preset resistance threshold, the reservoir is determined to be precipitated blockage; if the permeability resistance parameter is less than or equal to the preset resistance threshold, the reservoir is determined to be non-precipitated blockage.
[0097] When the seepage improvement parameter is greater than the preset improvement threshold and the mineral dissolution parameter is greater than the preset dissolution threshold, reservoir dissolution is determined.
[0098] Except for reservoir blockage and reservoir dissolution, the reservoir is considered to be in an inert or dissolution-precipitation equilibrium state.
[0099] As an example, the permeability resistance parameters, mineral dissolution parameters, and seepage improvement parameters are first mapped onto the X-axis, Y-axis, and Z-axis respectively to construct a ternary correlation evaluation model;
[0100] In the ternary correlation evaluation model, the preset improvement threshold is set to 0.95-1.05, and in this example, it is set to 1, which represents the critical value of improvement; the preset resistance threshold is set to 0.2, which represents the critical point of significant hindrance; and the preset dissolution threshold is set to 0.05, which represents the critical point of significant dissolution. The above thresholds can be determined based on the statistical median of the historical core experimental data of the reservoir, or empirically set according to the average pore throat radius and fluid viscosity of the target reservoir.
[0101] 1. When the seepage improvement parameter < 1, it indicates a significant decrease in seepage rate, classifying the reservoir as blocked. The blocking type is further divided into secondary precipitation blocking and non-precipitation blocking.
[0102] 1.1 If the permeation resistance parameter is >0.2, it indicates that the amorphous gel generated during the chemical flooding process is causing blockage. The reservoir is then determined to be blocked by secondary precipitation. This can be improved by adding dispersants, optimizing the injection rate, or reducing the molecular weight of the polymer.
[0103] 1.2 If the permeability resistance parameter is ≤0.2, it indicates that the possibility of secondary amorphous gel causing blockage during chemical flooding is relatively low. It may be due to clay mineral hydration expansion, particle migration or emulsion blockage (i.e., the sensitivity of the reservoir itself). In this case, the reservoir is determined to be non-precipitation type blockage, which can be improved by adding clay stabilizers or anti-swelling agents.
[0104] 2. When the seepage improvement parameter is greater than 1 and the mineral dissolution parameter is greater than 0.05, it indicates that the dissolution effect is significantly greater than the inhibition effect, and the reservoir is determined to be dissolved, which can maintain the current chemical flooding system.
[0105] 3. Except for reservoir blockage 1 and reservoir dissolution 2, the above situations are judged to be in an inert or dissolution-precipitation equilibrium state, that is, the chemical agent reacts weakly with the reservoir core, with no obvious dissolution or precipitation, and no change in physical properties; the alkali concentration can be appropriately increased or the surfactant can be replaced to enhance the oil washing efficiency and activate the reaction.
[0106] In another embodiment of the present invention, the secondary precipitation blockage under condition 1.1 can be further evaluated by combining the distribution and aggregation parameters assessed in step S3; for example, when the distribution and aggregation parameters are greater than 0.5, it is determined to be local precipitation blockage, otherwise it is whole-rock precipitation blockage.
[0107] In summary, this invention first assesses the systematic bias of different elemental determination systems using initial core samples unaffected by chemical displacement. Then, by combining the elemental abundance and mineral source abundance of major elements in reservoir minerals assessed by different elemental determination systems, it accurately analyzes the abundance gap, assesses the formation of secondary amorphous gels to determine permeability resistance parameters, assesses the improvement in permeability before and after chemical displacement to determine seepage improvement parameters, assesses the changes in mineral source abundance of preset major elements before and after chemical displacement to determine mineral dissolution parameters, and finally performs ternary correlation modeling to improve the accuracy of evaluating reservoir state changes.
[0108] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0109] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.
Claims
1. A ternary correlation modeling method for the synergistic effect of reservoir minerals and major elements, characterized in that, The method includes: Initial core samples were extracted from the reservoir core to obtain the pre-displacement permeability of the remaining reservoir core; after chemical displacement experiments were conducted on the remaining reservoir core, the post-displacement permeability was obtained, and the core samples were segmented and sampled to obtain segmented core samples; for each core sample, the elemental abundance and mineral source abundance of the preset major elements were obtained. Based on the deviation of the elemental abundance of the preset major elements in the initial core sample relative to the mineral source abundance, a measurement correction factor is obtained. Combined with the elemental abundance of the preset major elements and the mineral source abundance in each segment of the core sample, the residual abundance of secondary amorphous gel in each segment of the core sample is obtained. The permeability resistance parameters were obtained based on the residual abundance of secondary amorphous gel in each segmented core sample; the mineral dissolution parameters were obtained based on the difference between the mineral source abundance of the preset major elements in the initial core sample and the segmented core samples; and the seepage improvement parameters were determined based on the pre-drainage permeability and post-drainage permeability. Ternary correlation reservoir evaluation is based on permeability resistance parameters, mineral dissolution parameters, and seepage improvement parameters.
2. The ternary correlation modeling method for the synergistic effect of reservoir minerals and major elements according to claim 1, characterized in that, The methods for obtaining the elemental abundance and the mineral source abundance include: For each core sample, the contents of preset major elements, preset inert elements, and the mineral content of crystalline minerals containing preset major elements are determined. The preset inert elements do not include the preset major elements. Based on the mineral content of each crystalline mineral and the mass fraction of the preset major elements within it, the mineral source content of the preset major elements is determined. The element abundance of the preset major element is determined based on the ratio of the preset major element content to the preset inert element content; the mineral source abundance of the preset major element is determined based on the ratio of the mineral source content of the preset major element to the preset inert element content.
3. The ternary correlation modeling method for the synergistic effect of reservoir minerals and major elements according to claim 1, characterized in that, The method for obtaining the determination correction factor includes: The ratio of the abundance of the preset major elements in the initial core sample to the abundance of the mineral source is used as the determination correction factor.
4. The ternary correlation modeling method for the synergistic effect of reservoir minerals and major elements according to claim 1, characterized in that, The method for obtaining the residual abundance includes: For each segment of core sample, the mineral source abundance of the preset major elements is weighted using the determination correction factor, and the residual abundance of the secondary amorphous gel is determined based on the deviation of the weighted result from the elemental abundance of the preset major elements.
5. The ternary correlation modeling method for the synergistic effect of reservoir minerals and major elements according to claim 1, characterized in that, The method for obtaining the permeation resistance parameter includes: Distribution and aggregation parameters were obtained based on the distribution characteristics of the residual abundance of secondary amorphous gel in each segmented core sample. The residual blockage parameters were obtained based on the cumulative characteristics of the residual abundance of secondary amorphous gel in all segmented core samples. By combining the distribution aggregation parameters and the residual blockage parameters, the permeability resistance parameters of the remaining reservoir core are obtained.
6. The ternary correlation modeling method for the synergistic effect of reservoir minerals and major elements according to claim 5, characterized in that, The method for obtaining the distribution aggregation parameters includes: The uniform distribution probability is determined based on the proportion of a single segmented core sample in the remaining reservoir cores; the actual distribution probability is determined based on the proportion of the residual abundance of each segmented core sample in the sum of the residual abundance of all segmented core samples; and the distribution aggregation parameters are determined based on the difference between the uniform distribution probability and the actual distribution probability.
7. The ternary correlation modeling method for the synergistic effect of reservoir minerals and major elements according to claim 1, characterized in that, The methods for obtaining the mineral dissolution parameters include: Based on the average characteristics of the mineral source abundance of the preset major elements in all segmented core samples, the post-displacement mineral source abundance of the preset major elements after the chemical displacement experiment is determined; based on the difference between the mineral source abundance of the preset major elements in the initial core samples and the post-displacement mineral source abundance, mineral dissolution parameters are obtained.
8. The ternary correlation modeling method for the synergistic effect of reservoir minerals and major elements according to claim 1, characterized in that, The method for obtaining the seepage improvement parameters includes: Based on the ratio between the pre-drainage permeability and the post-drainage permeability, seepage improvement parameters are obtained.
9. The ternary correlation modeling method for the synergistic effect of reservoir minerals and major elements according to claim 1, characterized in that, Ternary correlation reservoir evaluation based on permeability resistance parameters, mineral dissolution parameters, and seepage improvement parameters includes: A ternary correlation evaluation model is constructed based on the aforementioned permeability resistance parameters, mineral dissolution parameters, and seepage improvement parameters: When the seepage improvement parameter is less than the preset improvement threshold, the reservoir is determined to be blocked; wherein, if the permeability resistance parameter is greater than the preset resistance threshold, the reservoir is determined to be subject to precipitation blockage; if the permeability resistance parameter is less than or equal to the preset resistance threshold, the reservoir is determined to be subject to non-precipitation blockage. When the seepage improvement parameter is greater than the preset improvement threshold and the mineral dissolution parameter is greater than the preset dissolution threshold, reservoir dissolution is determined. Except for reservoir blockage and reservoir dissolution, the reservoir is considered to be in an inert or dissolution-precipitation equilibrium state.
10. The ternary correlation modeling method for the synergistic effect of reservoir minerals and major elements according to claim 1, characterized in that, Segmentation sampling to obtain segmented core samples includes: After the chemical displacement experiment, core samples of uniform length were cut from the beginning, end and middle of the remaining reservoir core.