Method and system for evaluating change of oil layer profile in thick oil cold production process

Abstract

The application belongs to the technical field of tertiary oil recovery, and discloses a method and system for evaluating changes in oil layer profile production in a heavy oil cold production process, wherein the method comprises the following steps: determining a target reservoir; obtaining original characteristic chromatographic parameter and original biomarker parameter of each oil layer of the target reservoir; determining characteristic chromatographic parameter and biomarker parameter of each oil layer before and after injection of a viscosity increasing system; determining crude oil chromatographic characteristic change parameter and biomarker production change parameter of each oil layer; determining interlayer production change value, and judging improvement effect of the viscosity increasing system on oil layer profile production. The application fully utilizes characteristic chromatographic parameter of crude oil of different oil layers and biomarker parameter in produced water, can dynamically and accurately evaluate oil and water production effect of the viscosity increasing system on different oil layers, does not rely on indirect signals, is not easily disturbed, is convenient, efficient and low in cost.

Description

Technical Field

[0001] This invention belongs to the field of tertiary oil recovery technology, and specifically relates to a method and system for evaluating changes in reservoir profile during cold oil recovery. Background Technology

[0002] Developing environmentally friendly, efficient, and green enhanced oil recovery technologies has become an important direction for the oil and gas development field. Addressing the prominent issues of high energy consumption and large carbon emissions commonly found in current heavy oil development, existing technologies have been widely researched for green and low-carbon heavy oil cold recovery technologies, and many related technologies have been industrialized. Among these, effective mobility control of reservoir fluids, and the subsequent evaluation and dynamic regulation of these effects, are fundamental and crucial for ensuring development effectiveness and achieving efficient displacement.

[0003] Biopolymers are high-molecular-weight biological agents produced by fermentation of specific microorganisms, providing a new technological path for green oil and gas development. Compared with traditional chemically synthesized polymers, biopolymers have significant advantages such as strong temperature and salt resistance, unique viscoelasticity, outstanding rheological control capabilities, excellent emulsification properties, and environmental friendliness. Therefore, they have received widespread attention and application in the field of enhanced oil recovery. Whether biopolymers or traditional chemically synthesized polymers, their oil displacement mechanism mainly manifests in two aspects: first, by increasing the viscosity of injected water, improving the oil-water mobility ratio, and enhancing microscopic oil displacement efficiency; second, relying on the flow resistance formed by high-viscosity fluids in porous media, they preferentially enter and block high-permeability layers or water-channeling dominant channels, forcing subsequent displacement fluids to redirect to medium- and low-permeability oil-bearing areas that have not been effectively affected, thereby expanding the macroscopic swept volume.

[0004] In practical field applications, the rapid and accurate evaluation of the reservoir activation range of viscosity-enhancing and displacement systems such as biopolymers is crucial for real-time optimization of development plans and control of development costs. Only by accurately determining the underground migration path and area of ​​action of the viscosity-enhancing system can we effectively determine whether high-permeability channels are blocked and whether the displacement fluid has achieved effective diversion. This allows for the scientific adjustment of injection parameters, ensuring enhanced oil recovery and avoiding ineffective inputs. Since the produced fluids from production wells in the field typically come from multiple perforated sections, the oil enhancement effect brought about by the displacement of viscosity-enhancing systems is reflected in the mixed production of crude oil from multiple sections. The monitoring results of the mixed fluid are difficult to reflect the true location and displacement trajectory of the viscosity-enhancing system in each individual oil layer. Therefore, it is impossible to accurately identify at which layer the viscosity-enhancing system effectively expands the crude oil activation range, ultimately restricting the refined optimization of displacement plans and the layer-specific adjustments.

[0005] Currently, the evaluation methods for the spillover effect of viscosity-enhancing systems mainly include three categories: First, judging the sealing effect by monitoring injection pressure, with a continuous rise in pressure usually regarded as an indication that the high-permeability zone has been effectively sealed; second, inverting the dynamic response of production wells, taking the decrease in water cut and the increase in oil production as the direct basis for the expansion of the spillover volume; and third, using inter-well tracer monitoring to characterize the front edge of underground fluid migration and the spillover range.

[0006] However, the aforementioned conventional methods all have significant limitations in practical applications. The core problem lies in the strong indirectness and ambiguity of the evaluation results. For example, pressure changes may originate from near-wellbore blockage or contamination rather than the effect of deep viscosity-enhancing systems, and single-well production increases may be short-term phenomena rather than evidence of substantial and sustained expansion of the affected volume. There is a significant time lag between the implementation of injection measures and the production well generating a identifiable response, making rapid assessment difficult. Although tracer testing is relatively direct, it is costly and complex, making it unsuitable for frequent implementation as a routine monitoring method and difficult to achieve continuous dynamic description.

[0007] In summary, under the complex reservoir geology and dynamic development conditions in the field, multiple influencing factors are coupled with each other, which leads to the limitations of the assessment methods based on monitoring data. The indirect signals (pressure, production and water cut, etc.) on which the interlayer profile is used during the cold production of heavy oil are easily interfered with and have a delayed response. The cost of tracer testing is high and continuous monitoring is not possible. Summary of the Invention

[0008] To address the above problems, this invention provides a method for evaluating changes in reservoir profile activity during heavy oil cold recovery, comprising the following steps: Screen multi-layered heavy oil reservoirs for development and identify target reservoirs for cold production of combined heavy oil reservoirs. Obtain the original characteristic chromatographic parameters and original biomarker parameters of each oil layer in the target reservoir; Before injecting the thickening system, determine the characteristic chromatographic parameters and biomarker parameters of each oil layer before implementation, and after injecting the thickening system, determine the characteristic chromatographic parameters and biomarker parameters of each oil layer sampled. Based on the original characteristic chromatographic parameters, pre-implementation characteristic chromatographic parameters, and sampling characteristic chromatographic parameters of each oil layer, the crude oil chromatographic characteristic change parameters of each oil layer are determined; based on the original biomarker parameters, pre-implementation biomarker parameters, and sampling biomarker parameters, the biomarker activation change parameters of each oil layer are determined. Based on the changes in crude oil chromatographic characteristics, biomarker activation parameters, and production index values ​​for each oil layer, the interlayer activation change value is determined, and the improvement effect of the viscosity-enhancing system on the activation of the oil layer profile is judged by the interlayer activation change value.

[0009] Furthermore, the target reservoir meets the following requirements: reservoir temperature ≤120℃, permeability ≥50×10⁻³μm. 2 Formation water salinity ≤150000mg / L, number of oil-bearing layers extracted ≥2.

[0010] Furthermore, the original characteristic chromatographic parameters and original biomarker parameters of each oil layer in the target reservoir are obtained, including the following steps: The gas chromatograms of crude oil total hydrocarbons in each oil layer of the target reservoir were analyzed to screen for characteristic low and medium molecular weight hydrocarbon chromatographic peaks, and the original characteristic chromatographic parameters of each oil layer were calculated. For each original layer of the target reservoir, the produced water is subjected to microbial community or functional gene detection to determine the original biomarker parameters of each layer.

[0011] Furthermore, the gas chromatograms of crude oil total hydrocarbons in each layer of the target reservoir are analyzed to screen for characteristic low- and medium-molecular-weight hydrocarbon peaks, and the original characteristic chromatographic parameters of each layer are calculated, including the following steps: The gas chromatograms of crude oil total hydrocarbons in each oil layer of the target reservoir were analyzed to screen and identify characteristic low- and medium-molecular-weight hydrocarbon chromatographic peaks that can reflect the variation of crude oil composition in different oil layers. The characteristic low- and medium-molecular-weight hydrocarbon chromatographic peaks include the characteristic component chromatographic peaks with relatively high peak values ​​of carbon number less than 10, namely Ca, Cb, Cd, and Ce. The peak areas of different characteristic components were compared with each other, and the (Ca+Cb) / (Cd+Ce) chromatographic peak area ratio with the highest ratio was taken as the original characteristic chromatographic parameter of the oil layer.

[0012] Furthermore, for each original layer of the target reservoir, the produced water is subjected to microbial community or functional gene analysis to determine the original biomarker parameters of each layer, including the following steps: Microbial communities or functional genes were detected in the produced water of each original layer of the target reservoir. Marker microorganisms or marker functional genes that reflect the original mobilization degree of different oil layers were screened and identified. The ratio of the content of different marker microorganisms to the content of functional genes was used as the original biomarker parameter of each oil layer.

[0013] Furthermore, the characteristic chromatographic parameters and biomarker parameters of each oil layer sampled after the injection of the viscosity-enhancing system were determined, including the following steps: Cold production of heavy oil from the combined layers was carried out on the target reservoir, and a viscosity-enhancing system was injected. Characteristic chromatographic parameters of crude oil in the combined layer produced fluid were analyzed in chronological order to obtain characteristic chromatographic parameters of each oil layer sample. Biomarker analysis was performed on water in the combined layer produced fluid to obtain biomarker parameters of each oil layer sample.

[0014] Furthermore, based on the original characteristic chromatographic parameters of each oil layer, the characteristic chromatographic parameters before implementation, and the characteristic chromatographic parameters of the sampling, the crude oil chromatographic characteristic variation parameters of each oil layer are determined, including the following steps: The difference between the sampling characteristic chromatographic parameters of each oil layer and the characteristic chromatographic parameters before implementation is divided by the difference between the original characteristic chromatographic parameters and the characteristic chromatographic parameters before implementation to obtain the crude oil chromatographic characteristic change parameters of each oil layer.

[0015] Furthermore, based on the original biomarker parameters, pre-implementation biomarker parameters, and sampled biomarker parameters for each oil layer, the biomarker activation change parameters for each oil layer are determined, including the following steps: The difference between the sampled biomarker parameters of each oil layer and the pre-implementation biomarker parameters is divided by the difference between the original biomarker parameters and the pre-implementation biomarker parameters to obtain the biomarker activation change parameters for each oil layer.

[0016] Furthermore, based on the changes in crude oil chromatographic characteristics, biomarker mobilization parameters, and production index values ​​for each oil layer, the interlayer mobilization change value is determined, including the following steps: The crude oil chromatographic characteristic variation parameters, biomarker activation variation parameters, and production index variation parameters of each oil layer were standardized to obtain the corresponding standardized scores. The standardized score of each parameter is multiplied by its corresponding assigned weight and then summed to obtain the interlayer utilization change value; among which, the sum of the assigned weights of the crude oil chromatographic characteristic change parameter, the biomarker utilization change parameter, and the production indicator change parameter is 1.

[0017] This invention also provides a system for evaluating changes in reservoir profile during heavy oil cold production, comprising: The reservoir screening unit screens multi-layered heavy oil development reservoirs and identifies target reservoirs for implementing combined-layer heavy oil cold production. The crude oil characterization unit is used to obtain the original characteristic chromatographic parameters and original biomarker parameters of each oil layer in the target oil reservoir. The dynamic monitoring unit determines the characteristic chromatographic parameters and biomarker parameters of each oil layer before the injection of the thickening system, as well as the characteristic chromatographic parameters and biomarker parameters of each oil layer after the injection of the thickening system. The parameter calculation unit is used to determine the crude oil chromatographic characteristic change parameters of each oil layer based on the original characteristic chromatographic parameters, pre-implementation characteristic chromatographic parameters, and sampling characteristic chromatographic parameters of each oil layer; and to determine the biomarker activation change parameters of each oil layer based on the original biomarker parameters, pre-implementation biomarker parameters, and sampling biomarker parameters. The comprehensive evaluation unit is used to determine the interlayer utilization change value based on the changes in crude oil chromatographic characteristics, biomarker utilization change parameters, and production index change values ​​of each oil layer. The interlayer utilization change value is used to judge the improvement effect of the viscosity-enhancing system on the utilization of the oil layer profile.

[0018] Furthermore, the crude oil characteristic analysis unit is specifically used for: The gas chromatograms of crude oil total hydrocarbons in each oil layer of the target reservoir were analyzed to screen and identify characteristic low-to-medium molecular weight hydrocarbon peaks that could reflect the variation of crude oil composition in different oil layers. These characteristic low-to-medium molecular weight hydrocarbon peaks included peaks of relatively high carbon number (less than 10), such as Ca, Cb, Cd, and Ce. The peak areas of different characteristic components were compared, and the peak area ratio (Ca+Cb) / (Cd+Ce) with the highest ratio was taken as the original characteristic chromatographic parameter of the oil layer.

[0019] Furthermore, the comprehensive evaluation unit is specifically used for: The crude oil chromatographic characteristic variation parameters, biomarker activation variation parameters, and production index variation parameters of each oil layer were standardized to obtain the corresponding standardized scores. The standardized score of each parameter is multiplied by its corresponding assigned weight and then summed to obtain the interlayer utilization change value; among which, the sum of the assigned weights of the crude oil chromatographic characteristic change parameter, the biomarker utilization change parameter, and the production indicator change parameter is 1.

[0020] The beneficial effects of this invention are: 1. This invention fully utilizes the characteristic chromatographic parameters of crude oil from different oil layers and the biomarker parameters of produced water, which can dynamically and accurately evaluate the effect of the viscosity-enhancing system on the movement of oil and water in different oil layers. It does not rely on indirect signals (pressure, production and water cut, etc.), is not easily disturbed, is convenient, efficient and low in cost, and is suitable for real-time monitoring in heavy oil cold mining sites. Moreover, based on the on-site monitoring results, it can make timely directional adjustments to subsequent reservoir development measures.

[0021] 2. This invention only requires periodic sampling and testing of the produced fluid to complete the evaluation. The time interval from implementation to evaluation conclusion is short, and there is no significant lag problem associated with traditional tracers and pressure monitoring. It can track the entire process of heavy oil cold production and viscosity-enhancing system injection, achieving long-term, continuous dynamic monitoring of reservoir profile changes, and providing timely data support for real-time optimization of on-site development plans.

[0022] 3. Compared with the complex and expensive inter-well tracer testing technology, this invention adopts a combination of crude oil gas chromatography analysis, microbial detection and routine production data monitoring. The detection equipment is conventional, the sampling and analysis process is simple, the cost per test is low, and no complicated on-site construction is required. It can be carried out frequently as a daily monitoring method in oil fields and has a wider range of applications.

[0023] 4. This invention calculates the changes in crude oil chromatographic characteristics and biomarker mobilization parameters at different levels, separately determining the effectiveness of the thickening system from two dimensions: crude oil mobilization and formation water flow direction. These are then combined with weighted fusion of production indicator changes to obtain the interlayer mobilization change value. This multi-dimensional indicator fusion evaluation model comprehensively considers three types of information: formation fluids, microorganisms, and production dynamics, providing a complete evaluation dimension. Furthermore, based on the interlayer mobilization change value, it classifies the system into three levels: suitable, requiring adjustment, and unsuitable. The clear classification results directly guide technicians in judging the suitability of the thickening system and accurately making decisions such as layer adjustment, injection parameter optimization, and displacement system replacement.

[0024] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description and the drawings. Attached Figure Description

[0025] To more clearly illustrate the technical solutions 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart illustrating a method for evaluating changes in reservoir profile activation during heavy oil cold recovery according to an embodiment of the present invention is shown. Figure 2 A schematic diagram of a system for evaluating changes in reservoir profile during cold oil recovery according to an embodiment of the present invention is shown. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," etc., used in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein.

[0029] This invention provides a method and system for evaluating changes in reservoir profile activity during heavy oil cold production. It addresses the problems of existing methods relying on indirect signals (pressure, production, and water cut, etc.) for inter-layer profile activity during heavy oil cold production, which are susceptible to interference and have delayed responses; and the high cost and inability to continuously monitor tracers. This method avoids the situation where the mixed produced fluid from production wells completely masks the displacement effect of the viscosity-enhancing system in a specific single layer, making it impossible to determine the true activity range of each reservoir and severely restricting targeted optimization and adjustment of the reservoir system.

[0030] like Figure 1 As shown, a method for evaluating changes in reservoir profile during heavy oil cold production includes the following steps: S1. Screen multi-layered heavy oil reservoirs for development and identify target reservoirs for cold production of combined heavy oil reservoirs.

[0031] The target reservoirs must meet the following criteria: reservoir temperature ≤ 120℃, permeability ≥ 50 × 10⁻⁶. -3 μm 2 Formation water salinity ≤150000mg / L, number of oil-bearing layers extracted ≥2.

[0032] S2. Obtain the original characteristic chromatographic parameters and original biomarker parameters of each oil layer in the target reservoir, including the following steps: S21. Analyze the crude oil full hydrocarbon gas chromatograms of each oil layer in the target reservoir, screen for characteristic low- and medium-molecular-weight hydrocarbon chromatographic peaks, and calculate the original characteristic chromatographic parameters of each oil layer, including: The gas chromatograms of crude oil total hydrocarbons in each layer of the target reservoir were analyzed to screen and identify characteristic low-to-medium molecular weight hydrocarbon peaks that could reflect the variation patterns of crude oil composition in different layers. These characteristic low-to-medium molecular weight hydrocarbon peaks included relatively high peak values ​​for components with fewer than 10 carbon atoms, such as Ca, Cb, Cd, and Ce. The peak areas of different characteristic components were compared, and the peak area ratio (Ca+Cb) / (Cd+Ce) with the highest ratio was selected as the original characteristic chromatographic parameter SA of the oil layer. i 'i' represents the oil layer number of the target reservoir. The greater the difference in crude oil properties and mobility between different oil layers, the higher the SA value. i The greater the difference.

[0033] This step analyzes the original layered crude oil and screens for the highest value A, which represents the chromatographic peak area ratio of different oil layers. i = (Ca + Cb / Cd + Ce) is used as a chromatographic parameter reflecting the original characteristics of the oil reservoir, SA. i The identified characteristic low-to-medium molecular weight hydrocarbon chromatographic peaks were selected, which are characterized by stable ratios and strong comparability.

[0034] Wherein, (Ca+Cb) / (Cd+Ce) represents the ratio of the sum of the peak areas of the relatively high-peaking characteristic components Ca and Cb (with carbon numbers less than 10) to the sum of the peak areas of the relatively high-peaking characteristic components Cd and Ce (with carbon numbers greater than 10). Ca+Cb can represent one or two characteristic component peaks. Cd+Ce can also represent one or two characteristic component peaks.

[0035] S22. For each original layer of produced water in the target reservoir, conduct microbial community or functional gene analysis to determine the original biomarker parameters for each layer. This includes: conducting microbial community or functional gene analysis on the produced water of each original layer of the target reservoir, screening and identifying marker microorganisms (bacteria or archaea) or marker functional genes with stable and comparable ratios that strongly reflect the original mobilization degree of different layers, and using the ratio of different marker microorganism contents to functional gene contents as the original biomarker parameters for each layer. i Original biomarker parameters SB i The unique characteristic parameters reflecting the formation water of the oil reservoir clearly indicate that the greater the differences in crude oil properties and mobility between different oil reservoirs, the higher the SB (Soil Surface Water) level. i The greater the difference.

[0036] For example, the types and abundance of endogenous microorganisms vary greatly under different reservoir environments, with high-activity areas exhibiting both oxygen-consuming and facultative anaerobic microorganisms. Pseudomonadota , Arcobacter High abundance; low-activity anaerobic microorganisms Desulfovibrio , Methanobacteriota The abundance is relatively high. For crude oils with different properties, the corresponding genes for alkane degradation are: alkB, almA, ladA, P450 Etc., genes corresponding to the aerobic degradation of monocyclic aromatic hydrocarbons include todC1, tmoA, xylM, pheA Polycyclic aromatic hydrocarbons (PAHs) PAHs The corresponding genes are nahA, nidA, phn, bphA1 Etc., the genes corresponding to the anaerobic degradation of aromatic hydrocarbons include: assA, bssA, nms wait.

[0037] S3. Implement cold production of heavy oil in the target reservoir by combining layers, and determine the characteristic chromatographic parameters and biomarker parameters of each oil layer before the injection of the viscosity-enhancing system; determine the characteristic chromatographic parameters and biomarker parameters of each oil layer after the injection of the viscosity-enhancing system.

[0038] This included determining the characteristic chromatographic parameters and pre-implementation biomarker parameters for each oil layer before injecting the viscosity-enhancing system. Specifically, before injecting the viscosity-enhancing system into the target reservoir, the crude oil in the co-layer produced fluid mainly originated from high-permeability oil layers. The characteristic chromatographic peak ratio of each oil layer was determined as the characteristic chromatographic parameter MA for each oil layer before implementation. iThe biomarker values ​​for each oil layer were determined as pre-implementation biomarker parameters (MB). i The thickening system can be a biopolymer or the like.

[0039] The process of determining the characteristic chromatographic parameters and biomarker parameters of each oil layer after injecting the viscosity-enhancing system includes: performing cold production of heavy oil in the target reservoir by combining layers and injecting the viscosity-enhancing system, and analyzing the characteristic chromatographic parameters of the crude oil in the produced fluid of the combined layers in chronological order to obtain the characteristic chromatographic parameters of each oil layer; and performing biomarker analysis on the water in the produced fluid of the combined layers to obtain the biomarker parameters of each oil layer.

[0040] S4. Based on the original characteristic chromatographic parameters, pre-implementation characteristic chromatographic parameters, and sampling characteristic chromatographic parameters of each oil layer, determine the crude oil chromatographic characteristic change parameters of each oil layer; based on the original biomarker parameters, pre-implementation biomarker parameters, and sampling biomarker parameters, determine the biomarker activation change parameters of each oil layer.

[0041] Specifically, based on the original characteristic chromatographic parameters, pre-implementation characteristic chromatographic parameters, and sampling characteristic chromatographic parameters of each oil layer, the crude oil chromatographic characteristic variation parameters of each oil layer are determined. This includes dividing the difference between the sampling characteristic chromatographic parameters and the pre-implementation characteristic chromatographic parameters of each oil layer by the difference between the original characteristic chromatographic parameters and the pre-implementation characteristic chromatographic parameters to obtain the crude oil chromatographic characteristic variation parameters of each oil layer, as detailed below: P i =(QA i -MA i ) / (SA i -MA i )×100% In the formula, P i QA represents the variation parameters of crude oil chromatographic characteristics in the i-th oil layer. i MA represents the characteristic chromatographic parameters of the i-th oil layer. i SA represents the characteristic chromatographic parameters of the i-th oil layer before implementation. i This represents the original characteristic chromatographic parameters of the i-th oil layer.

[0042] When P i When P ≥ 50%, it indicates that the high-permeability oil layer has been blocked, and the crude oil in the low-permeability oil layer begins to be effectively utilized. The characteristic chromatographic parameters of the crude oil change towards the chromatographic parameters of the low-permeability oil layer, indicating that the viscosity-enhancing system has a high utilization range for interlayer crude oil, suggesting that the viscosity-enhancing system is suitable for field application requirements; when P i When the viscosity is less than 50%, the characteristic chromatographic parameters of crude oil change weakly towards lower permeability, indicating that the thickening system does not effectively utilize the interlayer crude oil. This suggests that the system is not suitable for field application requirements and should be replaced.

[0043] Specifically, based on the original biomarker parameters, pre-implementation biomarker parameters, and sampled biomarker parameters for each oil layer, the biomarker activation change parameters for each oil layer are determined. This includes dividing the difference between the sampled biomarker parameters and the pre-implementation biomarker parameters for each oil layer by the difference between the original biomarker parameters and the pre-implementation biomarker parameters to obtain the biomarker activation change parameters for each oil layer, as detailed below: G i =(QB i -MB i ) / (SB i -MB i )×100% In the formula, G i QB represents the biomarker mobilization variation parameter of the i-th oil layer. i MB represents the sampling biomarker parameter of the i-th oil layer. i SB represents the pre-implementation biomarker parameters for the i-th oil layer. i This represents the original biomarker parameters of the i-th oil layer.

[0044] When G i A concentration of ≥50% indicates a high utilization range of interlayer formation water by the viscosity-enhancing system, suggesting that the system achieves significant fluid flow redirection; when G i <50% indicates that the viscosity-enhancing system has a weak ability to redirect fluid flow.

[0045] S5. Based on the changes in crude oil chromatographic characteristics, biomarker activation parameters, and production index values ​​of each oil layer, determine the interlayer activation value, and judge the improvement effect of the viscosity-enhancing system on the activation of the oil layer profile by the interlayer activation value.

[0046] The changes in production indicators were obtained through dynamic monitoring of oil well production in the field. Throughout the entire process of heavy oil cold recovery and viscosity-enhancing system injection, simultaneous sampling of crude oil and formation water was conducted using online wellhead metering equipment and manual metering methods to continuously monitor core production data such as daily oil production and water cut of the target wells. Stable production data before the implementation of the viscosity-enhancing system was used as a benchmark to calculate the variation range of production indicators at different production stages. The raw production indicator data were standardized to eliminate dimensions, yielding the parameters for changes in production indicators.

[0047] The determination of interlayer utilization changes, based on the changes in crude oil chromatographic characteristics, biomarker utilization parameters, and production index values ​​for each oil layer, includes the following steps: The variation parameter P of crude oil chromatographic characteristics for each oil layer i Biomarker mobilization change parameter G i Production indicator change parameter Ki Each parameter is standardized to obtain a standardized score. The standardized score of each parameter is multiplied by its corresponding weight and then summed to obtain the interlayer mobilization change value F. The sum of the weights assigned to the crude oil chromatographic characteristic change parameter, the biomarker mobilization change parameter, and the production indicator change parameter is 1.

[0048] This step involves hierarchical data fusion and uses standardized processing of the crude oil chromatographic characteristic parameter P. i Changes in produced water microbial markers, parameter G i Changes in production indicators K i To eliminate dimensional differences, weight allocation is carried out based on the actual situation of the reservoir, and the interlayer dynamic change F is obtained by fusion calculation.

[0049] Among them, the improvement effect of the viscosity enhancement system on the reservoir profile utilization is judged by the interlayer utilization change value. The interlayer utilization change value is compared with the threshold, and the improvement effect of the viscosity enhancement system on the reservoir profile utilization is determined according to the comparison result. For example, F≥4 indicates that the improvement of the profile utilization is large, the viscosity enhancement system has an excellent improvement effect on the reservoir profile utilization, and the viscosity enhancement system is suitable. 2≤F<4 indicates that the improvement of the profile balance does not meet the development expectations, and the viscosity enhancement system needs to be adjusted. F<2 indicates that the improvement effect of the profile utilization is poor, and the viscosity enhancement system is not suitable.

[0050] For example, oil well 1 has a reservoir temperature of 50℃ and a permeability of (80~1200)×10 -3 μm 2 The formation water salinity is 15000 mg / L. Three oil layers were extracted. Crude oil from the three oil layers in well 1 was subjected to full hydrocarbon gas chromatography analysis. Based on the ratio of different chromatographic peak areas, the characteristic chromatographic parameter SA of the three oil layers was identified. 01 SA 02 SA 03 Based on the produced water from the three oil layers in well 1, biological community analysis and functional gene analysis were conducted. According to the ratios of different marker microorganisms and functional genes, the biomarker parameter SB of the three oil layers was identified. 01 SB 02 SB 03 The specific details are shown in Table 1.

[0051] Among them, SA in oil layer 1 01 =(C9+C 10 ) / C 20 =1.93、SB 01 = alm A / mcr A=3.1; SA in oil layer 2 02 =(C5+C6) / (C 17 +C 18=2.10, SB 02 =P450 / ( dsr A+ dsr B)=4.5; SA in oil layer 3 03 =(C 11 +C 12 ) / C 20 =1.60、SB 03 = alk B / 16SrDNA=0.565.

[0052] Table 1

[0053] Before the injection of the viscosity-enhancing system, most of the injected water entered the high-permeability oil layer 3, while the exploitation range of oil layers 1 and 2 was limited. The crude oil in the produced fluid mainly came from oil layer 3. Before implementation, the characteristic chromatographic parameters MA1=1.20, MA2=1.30, and MA3=1.50. After the viscosity-enhancing system was injected into the reservoir, QA1 and QA2 increased to 1.50 and 1.90, respectively, while QA3 decreased to 1.00. The peak value of P2 increased by 77.88%, and the peak oil production was 4.3t, as shown in Table 2.

[0054] This shows that the viscosity-enhancing system blocked the high-permeability layer, and both the crude oil and formation water in oil layer 2 were effectively utilized. The utilization rate of crude oil in oil layer 1 was limited, while the formation water was utilized to a certain extent.

[0055] Table 2

[0056] Hierarchical data fusion was carried out, and standardized processing of crude oil chromatographic characteristic variation parameter P was adopted. i Microbial biomarker change parameter G i Changes in production indicators K i To eliminate dimensional differences, weight allocation was carried out, and the interlayer mobility change F was obtained through fusion calculation. It was set that F≥4 is suitable for the viscosity-enhancing system, 2≤F<4 means that the scheme needs to be adjusted, and F<2 means that it is not suitable. The calculated F value of well 1 is 5, and the viscosity-enhancing system is suitable.

[0057]

[0058] In the formula, This represents the variation parameter P of the chromatographic characteristics of crude oil. i Biomarker mobilization change parameter G i Production indicator change parameter K i The standardized score of ) This represents the variation parameter P of the chromatographic characteristics of crude oil. iBiomarker mobilization change parameter G i Production indicator change parameter K i The weights assigned, Indicates the parameter number. =1,2…n, the dynamic changes between layers of oil well 1 were calculated, as shown in Table 3.

[0059] Table 3

[0060] For example, well 2 has a reservoir temperature of 30℃ and a permeability of (200~1000)×10 -3 μm 2 The formation water salinity is 8000 mg / L. Two oil layers were extracted. Crude oil from the two oil layers in well 2 was subjected to full hydrocarbon gas chromatography analysis. Based on the ratio of different chromatographic peak areas, the characteristic chromatographic parameter SA of the two oil layers was identified. 01 SA 02 Based on the produced water from the two oil layers in well 2, biological community analysis and functional gene analysis were conducted. According to the ratios of different marker microorganisms and functional genes, the biomarker parameter SB of the two oil layers was identified. 01 SB 02 The specific details are shown in Table 4. Among them, SA in oil layer 1... 01 =C9 / C 20 =1.50、SB 01 = mcr A / alm A=3.1; SA in oil layer 2 02 =C9 / (C 15 +C 16 =1.93, SB 02 =( dsr A+ dsr B) / P450=4.53.

[0061] Table 4

[0062] Before the injection of the viscosity-enhancing system, most of the injected water entered the oil layer 2 with higher permeability, and the utilization range of oil layer 1 was limited. The crude oil in the produced fluid mainly came from oil layer 2. Before implementation, the characteristic chromatographic parameter MA1=1.10 and the characteristic chromatographic parameter MA2=1.83. After the injection of the viscosity-enhancing system, QA1 increased to 1.4, QA2 decreased to 1.3, the peak value of P1 increased by 47.5%, and the peak value of oil increase was 2.5t, as shown in Table 5.

[0063] This shows that the viscosity-enhancing system blocked the high-permeability layer, and the formation water in oil layer 1 was effectively utilized, and the crude oil was utilized to a certain extent.

[0064] Table 5

[0065] Hierarchical data fusion was carried out, and standardized processing of crude oil chromatographic characteristic variation parameter P was adopted. i Microbial biomarker change parameter G i Changes in production indicators K i To eliminate dimensional differences, weight allocation was carried out, and the interlayer mobility change F was obtained through fusion calculation. The interlayer mobility change of oil well 2 was calculated, as shown in Table 6. It was set that F≥4 is suitable for the viscosity-enhancing system, 2≤F<4 means that the scheme needs to be adjusted, and F<2 means that it is not suitable. The calculated F value of well 2 is 2.6, and the viscosity-enhancing system needs to be adjusted.

[0066] Table 6

[0067] Based on the above-mentioned evaluation method for reservoir profile changes during heavy oil cold production, such as Figure 2 As shown in the figure, this embodiment of the invention also provides an evaluation system for changes in reservoir profile activity during the cold production of heavy oil, including a reservoir screening unit, a crude oil characteristic analysis unit, a dynamic monitoring unit, a parameter calculation unit, and a comprehensive evaluation unit.

[0068] The reservoir screening unit screens multi-layered heavy oil development reservoirs and identifies target reservoirs for combined heavy oil cold production. The crude oil characterization unit is used to obtain the original characteristic chromatographic parameters and original biomarker parameters of each oil layer in the target reservoir.

[0069] The dynamic monitoring unit determines the characteristic chromatographic parameters and biomarker parameters of each oil layer before injection of the thickening system, as well as the characteristic chromatographic parameters and biomarker parameters of each oil layer after injection of the thickening system.

[0070] The parameter calculation unit is used to determine the crude oil chromatographic characteristic change parameters of each oil layer based on the original characteristic chromatographic parameters, pre-implementation characteristic chromatographic parameters, and sampling characteristic chromatographic parameters of each oil layer; and to determine the biomarker activation change parameters of each oil layer based on the original biomarker parameters, pre-implementation biomarker parameters, and sampling biomarker parameters.

[0071] The comprehensive evaluation unit is used to determine the interlayer utilization change value based on the changes in crude oil chromatographic characteristics, biomarker utilization change parameters, and production index change values ​​of each oil layer. The interlayer utilization change value is used to judge the improvement effect of the viscosity-enhancing system on the utilization of the oil layer profile.

[0072] The method for evaluating changes in reservoir profile mobilization during heavy oil cold production, as described in this invention, is applicable to microbial or chemical cold production of medium-to-high permeability, medium-to-low temperature, and highly heterogeneous heavy oil. It improves oil displacement efficiency while expanding the affected system. Compared to existing technologies, this invention fully utilizes characteristic chromatographic parameters of crude oil from different reservoirs and biomarker parameters in produced water. It can dynamically and accurately evaluate the effects of the viscosity-enhancing system on the mobilization of oil and water in different reservoirs. It is convenient, efficient, and low-cost, suitable for real-time monitoring in heavy oil cold production sites. Furthermore, based on the on-site monitoring results, it allows for timely adjustments to subsequent reservoir development measures.

[0073] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating changes in reservoir profile activity during cold oil recovery, characterized in that, Includes the following steps: Screen multi-layered heavy oil reservoirs for development and identify target reservoirs for cold production of combined heavy oil reservoirs. Obtain the original characteristic chromatographic parameters and original biomarker parameters for each oil layer of the target reservoir, including, For each original layer of the target reservoir, the produced water is subjected to microbial community or functional gene detection to determine the original biomarker parameters of each oil layer; the original biomarker parameters of each oil layer are the ratio of the content of different marker microorganisms to the content of functional genes. Before injecting the thickening system, determine the characteristic chromatographic parameters and biomarker parameters of each oil layer before implementation, and after injecting the thickening system, determine the characteristic chromatographic parameters and biomarker parameters of each oil layer sampled. Based on the original characteristic chromatographic parameters, pre-implementation characteristic chromatographic parameters, and sampling characteristic chromatographic parameters of each oil layer, the crude oil chromatographic characteristic variation parameters of each oil layer are determined. Specifically, the difference between the sampling characteristic chromatographic parameters and the pre-implementation characteristic chromatographic parameters of each oil layer is divided by the difference between the original characteristic chromatographic parameters and the pre-implementation characteristic chromatographic parameters to obtain the crude oil chromatographic characteristic variation parameters of each oil layer. Q i =(QA i -MA i ) / (SA i -MA i )×100% In the formula, P i QA represents the variation parameters of crude oil chromatographic characteristics in the i-th oil layer. i MA represents the characteristic chromatographic parameters of the i-th oil layer. i SA represents the characteristic chromatographic parameters of the i-th oil layer before implementation. i These represent the original characteristic chromatographic parameters of the i-th oil layer; Based on the original biomarker parameters, pre-implementation biomarker parameters, and sampled biomarker parameters, the biomarker activation change parameters for each oil layer were determined. Specifically, the difference between the sampled biomarker parameters and the pre-implementation biomarker parameters for each oil layer was divided by the difference between the original biomarker parameters and the pre-implementation biomarker parameters to obtain the biomarker activation change parameters for each oil layer. G i =(QB i -MB i ) / (SB i -MB i )×100% In the formula, G i QB represents the biomarker mobilization variation parameter of the i-th oil layer. i MB represents the sampling biomarker parameter of the i-th oil layer. i SB represents the pre-implementation biomarker parameters for the i-th oil layer. i Represents the original biomarker parameters of the i-th oil layer; Based on the changes in crude oil chromatographic characteristics, biomarker activation parameters, and production index values ​​for each oil layer, the interlayer activation change value is determined, and the improvement effect of the viscosity-enhancing system on the activation of the oil layer profile is judged by the interlayer activation change value.

2. The method for evaluating changes in reservoir profile during heavy oil cold production according to claim 1, characterized in that, The target reservoir meets the following requirements: reservoir temperature ≤120℃, permeability ≥50×10⁻⁶. -3 μm 2 Formation water salinity ≤150000mg / L, number of oil-bearing layers extracted ≥2.

3. The method for evaluating changes in reservoir profile during heavy oil cold production according to claim 1, characterized in that, Obtaining the original characteristic chromatographic parameters and original biomarker parameters for each oil layer in the target reservoir also includes the following steps: The gas chromatograms of crude oil total hydrocarbons in each oil layer of the target reservoir were analyzed to screen for characteristic low and medium molecular weight hydrocarbon chromatographic peaks, and the original characteristic chromatographic parameters of each oil layer were calculated.

4. The method for evaluating changes in reservoir profile during heavy oil cold production according to claim 3, characterized in that, The gas chromatograms of crude oil total hydrocarbons in each layer of the target reservoir were analyzed to screen for characteristic low- and medium-molecular-weight hydrocarbon peaks, and the original characteristic chromatographic parameters of each layer were calculated, including the following steps: The gas chromatograms of crude oil total hydrocarbons in each oil layer of the target reservoir were analyzed to screen and identify characteristic low-to-medium molecular weight hydrocarbon peaks that could reflect the variation of crude oil composition in different oil layers. These characteristic low-to-medium molecular weight hydrocarbon peaks included peaks of relatively high carbon number (less than 10), such as Ca, Cb, Cd, and Ce. The peak areas of different characteristic components were compared, and the peak area ratio (Ca+Cb) / (Cd+Ce) with the highest ratio was taken as the original characteristic chromatographic parameter of the oil layer.

5. The method for evaluating changes in reservoir profile during heavy oil cold production according to claim 3, characterized in that, For each original layer of produced water in the target reservoir, microbial community or functional gene analysis is performed to determine the original biomarker parameters of each layer, including the following steps: Microbial communities or functional genes were detected in the produced water of each original layer of the target reservoir. Marker microorganisms or marker functional genes that reflect the original mobilization degree of different oil layers were screened and identified. The ratio of the content of different marker microorganisms to the content of functional genes was used as the original biomarker parameter of each oil layer.

6. The method for evaluating changes in reservoir profile during heavy oil cold production according to claim 1, characterized in that, After injecting the viscosity-enhancing system, the characteristic chromatographic parameters and biomarker parameters of each oil layer were determined, including the following steps: Cold production of heavy oil from the combined layers was carried out on the target reservoir, and a viscosity-enhancing system was injected. Characteristic chromatographic parameters of crude oil in the combined layer produced fluid were analyzed in chronological order to obtain characteristic chromatographic parameters of each oil layer sample. Biomarker analysis was performed on water in the combined layer produced fluid to obtain biomarker parameters of each oil layer sample.

7. The method for evaluating changes in reservoir profile during heavy oil cold production according to any one of claims 1-6, characterized in that, Based on the changes in crude oil chromatographic characteristics, biomarker activation parameters, and production index values ​​for each oil layer, the interlayer activation change value is determined, including the following steps: The crude oil chromatographic characteristic variation parameters, biomarker activation variation parameters, and production index variation parameters of each oil layer were standardized to obtain the corresponding standardized scores. The standardized score of each parameter is multiplied by its corresponding assigned weight and then summed to obtain the interlayer utilization change value; among which, the sum of the assigned weights of the crude oil chromatographic characteristic change parameter, the biomarker utilization change parameter, and the production indicator change parameter is 1.

8. A system for evaluating changes in reservoir profile activity during heavy oil cold production, characterized in that, include: The reservoir screening unit screens multi-layered heavy oil development reservoirs and identifies target reservoirs for implementing combined-layer heavy oil cold production. The crude oil characterization unit is used to obtain the original characteristic chromatographic parameters and original biomarker parameters of each oil layer in the target reservoir; including, For each original layer of the target reservoir, the produced water is subjected to microbial community or functional gene detection to determine the original biomarker parameters of each oil layer; the original biomarker parameters of each oil layer are the ratio of the content of different marker microorganisms to the content of functional genes. The dynamic monitoring unit determines the characteristic chromatographic parameters and biomarker parameters of each oil layer before the injection of the thickening system, as well as the characteristic chromatographic parameters and biomarker parameters of each oil layer after the injection of the thickening system. The parameter calculation unit is used to determine the crude oil chromatographic characteristic variation parameters for each oil layer based on the original characteristic chromatographic parameters, pre-implementation characteristic chromatographic parameters, and sampling characteristic chromatographic parameters for each oil layer. Specifically, the difference between the sampling characteristic chromatographic parameters and the pre-implementation characteristic chromatographic parameters for each oil layer is divided by the difference between the original characteristic chromatographic parameters and the pre-implementation characteristic chromatographic parameters to obtain the crude oil chromatographic characteristic variation parameters for each oil layer. Q i =(QA i -MA i ) / (SA i -MA i )×100% In the formula, P i QA represents the variation parameters of crude oil chromatographic characteristics in the i-th oil layer. i MA represents the characteristic chromatographic parameters of the i-th oil layer. i SA represents the characteristic chromatographic parameters of the i-th oil layer before implementation. i These represent the original characteristic chromatographic parameters of the i-th oil layer; Based on the original biomarker parameters, pre-implementation biomarker parameters, and sampled biomarker parameters, the biomarker activation change parameters for each oil layer were determined. Specifically, the difference between the sampled biomarker parameters and the pre-implementation biomarker parameters for each oil layer was divided by the difference between the original biomarker parameters and the pre-implementation biomarker parameters to obtain the biomarker activation change parameters for each oil layer. G i =(QB i -MB i ) / (SB i -MB i )×100% In the formula, G i QB represents the biomarker mobilization variation parameter of the i-th oil layer. i MB represents the sampling biomarker parameter of the i-th oil layer. i SB represents the pre-implementation biomarker parameters for the i-th oil layer. i Represents the original biomarker parameters of the i-th oil layer; The comprehensive evaluation unit is used to determine the interlayer utilization change value based on the changes in crude oil chromatographic characteristics, biomarker utilization change parameters, and production index change values ​​of each oil layer. The interlayer utilization change value is used to judge the improvement effect of the viscosity-enhancing system on the utilization of the oil layer profile.

9. The reservoir profile dynamics change evaluation system during heavy oil cold production according to claim 8, characterized in that, The crude oil characteristic analysis unit is specifically used for: The gas chromatograms of crude oil total hydrocarbons in each oil layer of the target reservoir were analyzed to screen and identify characteristic low-to-medium molecular weight hydrocarbon peaks that could reflect the variation of crude oil composition in different oil layers. These characteristic low-to-medium molecular weight hydrocarbon peaks included peaks of relatively high carbon number (less than 10), such as Ca, Cb, Cd, and Ce. The peak areas of different characteristic components were compared, and the peak area ratio (Ca+Cb) / (Cd+Ce) with the highest ratio was taken as the original characteristic chromatographic parameter of the oil layer.

10. The reservoir profile dynamics change evaluation system during heavy oil cold production according to claim 8, characterized in that, The comprehensive evaluation unit is specifically used for: The crude oil chromatographic characteristic variation parameters, biomarker activation variation parameters, and production index variation parameters of each oil layer were standardized to obtain the corresponding standardized scores. The standardized score of each parameter is multiplied by its corresponding assigned weight and then summed to obtain the interlayer utilization change value; among which, the sum of the assigned weights of the crude oil chromatographic characteristic change parameter, the biomarker utilization change parameter, and the production indicator change parameter is 1.

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