A method for predicting microbial rock sweet spot reservoirs

Abstract

The present invention discloses a method for predicting sweet spot reservoirs of microbial rocks. The method comprises the following steps: S100, using paleogeomorphological restoration technology based on well-seismic constraints to divide the microbial rock reservoir into sedimentary phase zones to determine favorable phase zones; S200, within the favorable phase zones, using seismic attribute analysis technology based on forward simulation to determine reservoir distribution to determine reservoir development zones; S300, within the reservoir development zones, using reservoir physical property inversion technology based on mud and silicon removal to determine sweet spot reservoirs. The present invention utilizes new geological knowledge, fully considers the three aspects of the main controlling factors of microbial rocks (lithophanes, karstification and rock fabric), and proposes a new idea for predicting sweet spot reservoirs with step-by-step control, especially carrying out porosity prediction, solving the influence of different microbial rock fabrics on the quality of reservoirs, and greatly improving the accuracy of reservoir prediction.

Description

Technical Field

[0001] The invention relates to a method for predicting carbonate reservoirs in the petroleum industry, and in particular to a method for predicting microbial rock sweet spot reservoirs. Background Art

[0002] Since Burne and Moor proposed the concept of microbial rocks in 1987, domestic and foreign scholars have conducted in-depth research on the bio-sedimentary structure, morphological characteristics, type classification and sedimentary environment of microbial rocks. Studies have shown that microbial rocks are widely distributed, mainly developed in older strata, buried deep, and can be used as good oil and gas reservoirs, containing rich oil and gas resources. At present, oil and gas fields of varying sizes have been discovered in microbial rocks in East Siberia, Russia, the Gulf of Mexico, the Santos Basin, Brazil, the Oman Salt Basin, the Tengiz Oilfield in Kazakhstan, and the Bohai Bay, Sichuan, and Tarim Basins in China.

[0003] With the advancement of engineering technology and the improvement of the exploration level of medium and shallow layers, deep marine carbonate rocks have become one of the important oil and gas exploration areas in my country in the future, and microbial rocks have become an important exploration target. In recent years, the Sinian Dengying Formation in the Sichuan Basin has made a major breakthrough, and reserves of more than one trillion cubic meters have been discovered in microbial rocks, which has attracted great attention from a large number of scholars, and a lot of research work has been carried out on the microbial rock reservoirs in this area. Studies have shown that the microbial rocks of the Dengying Formation mainly develop stromatolite, agglomerated dolomite, laminar dolomite and foamy sponge dolomite. The reservoirs are generally developed, with thin single-layer thickness, but large cumulative thickness, strong heterogeneity, and obvious differences in the vertical and horizontal directions. The degree of reservoir development is controlled by lithofacies and karstification. In the early stage, reservoir distribution prediction was carried out by karst paleo-geomorphological restoration and reservoir thickness prediction was carried out simply by reservoir inversion. The wells deployed based on this had low single-well production and were not consistent with actual drilling.

[0004] Deep ancient marine carbonate rocks have gradually become one of the important oil and gas exploration areas in my country. Microbial reservoirs are one of the main reservoir types. It is imperative to carry out research on the prediction technology of this type of reservoir. Accurately predicting the sweet spot of microbial reservoirs is of great significance for finding high-yield enrichment areas and increasing the production of single wells. Therefore, it is necessary to further improve the prediction accuracy of microbial reservoirs.

[0005] According to the latest research results, the development degree of the microbial reservoir of the Dengying Formation is not only controlled by lithofacies and karstification, but also by the rock structure of the microbial rock, that is, different rock types have different reservoir space types and different porosities. Therefore, based on the main controlling factors of the development of microbial reservoirs, the present invention proposes a new idea of ​​step-by-step control of "paleogeomorphological restoration and zoning based on well-seismic constraints, seismic attribute analysis and zoning based on forward simulation, and reservoir physical property inversion and point selection based on mud and silicon removal", forming a new method for predicting sweet spot reservoirs of microbial rock. Summary of the invention

[0006] The object of the present invention is to provide a method for predicting a microbial rock sweet spot reservoir, which is a step-by-step controlled sweet spot reservoir prediction method, and further improves the prediction accuracy of the microbial rock reservoir.

[0007] In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0008] The present invention provides a method for predicting a microbial rock sweet spot reservoir, comprising the following steps:

[0009] S100. Use paleo-geomorphological restoration technology based on well-seismic constraints to divide the microbial reservoir into sedimentary facies zones to determine favorable facies zones;

[0010] S200, within the favorable phase zone, using a seismic attribute analysis technique based on forward simulation to determine reservoir distribution to determine a reservoir development zone;

[0011] S300: Determine the sweet spot reservoir in the reservoir development area by using reservoir physical property inversion technology based on desludging and desiliconization.

[0012] The above three steps are carried out step by step. S100 is used to delineate the distribution range of the favorable phase belt; within the favorable phase belt, S200 is used to determine the distribution range of the reservoir; within the reservoir development area, S300 is used to predict the distribution range of the sweet spot reservoir.

[0013] The following is a detailed description of each step:

[0014] S100. Use paleogeomorphological restoration technology based on well-seismic constraints to divide the microbial reservoir into sedimentary facies zones to determine favorable facies zones.

[0015] According to the method of the present invention, preferably, the process of dividing the sedimentary facies zones of the microbial reservoir in S100 includes:

[0016] Based on the single well sedimentary facies analysis, well-seismic calibration is performed to clarify the paleo-geomorphic features and seismic reflection characteristics of different sedimentary facies belts; through paleo-geomorphic restoration, combined with seismic attribute analysis and well-seismic constraints, the sedimentary facies belts of the microbial rock reservoir are divided.

[0017] According to the method of the present invention, preferably, single well sedimentary phase analysis is carried out based on data such as cores, thin sections, logging, drilling and testing. Therefore, data such as cores, thin sections, logging, drilling and testing need to be obtained before S100.

[0018] According to the method of the present invention, preferably, the paleo-geomorphology restoration is carried out using the residual stratigraphic thickness method.

[0019] According to the method of the present invention, preferably, the favorable phase zone in S100 is a hill-beach phase.

[0020] S200. Determine reservoir distribution in the favorable phase zone using seismic attribute analysis technology based on forward modeling to determine reservoir development areas.

[0021] According to the method of the present invention, preferably, the process of determining reservoir distribution by using seismic attribute analysis technology based on forward simulation in S200 to determine reservoir development areas includes:

[0022] Through well-seismic calibration, the reservoir seismic response model is established; based on forward simulation, the reservoir development model corresponding to different seismic response characteristics is clarified; sensitive seismic attributes are selected (optimized), and multi-attribute fusion is performed to determine the distribution of favorable reservoir development areas.

[0023] According to the method of the present invention, preferably, the process of determining reservoir distribution by using seismic attribute analysis technology based on forward simulation in S200 to determine reservoir development areas includes:

[0024] Through well seismic calibration, reservoir seismic response modes are established: "wide trough + double peaks", "wide trough", "wide trough + single peak", and the relationship between different seismic response modes and production capacity is established: the "wide trough + double peaks" response mode is most likely to be high-yield;

[0025] A geological model with different reservoir thicknesses and reservoir development degrees above and below the siliceous layer was established to carry out forward simulation and clarify the reservoir development patterns corresponding to different seismic response characteristics. Based on the seismic response characteristics of the three reservoir development patterns, sensitive seismic attributes were selected and the corresponding sensitive attributes were extracted respectively. Then, the seismic attributes were fused to determine the distribution of favorable reservoir development areas.

[0026] According to the method of the present invention, preferably, a neural network algorithm is used to optimize sensitive seismic attributes.

[0027] S300: Determine the sweet spot reservoir in the reservoir development area by using reservoir physical property inversion technology based on desludging and desiliconization.

[0028] According to the method of the present invention, preferably, the process of determining the sweet spot reservoir by using the reservoir physical property inversion technology based on desludging and desiliconization in S300 includes:

[0029] First, the inversion method is selected; second, wave impedance inversion and natural gamma and siliceous content inversion are carried out; then, the three sets of inversion data bodies are used for calculation to obtain the wave impedance data body after eliminating the influence of mud and siliceous lithology; according to the relationship between wave impedance and porosity after eliminating the influence of lithology, the wave impedance data body after eliminating the influence of lithology is converted into porosity data body; finally, the sweet spot reservoir development area is determined according to the sweet spot reservoir porosity standard.

[0030] According to the method of the present invention, preferably, the calculation process of the wave impedance data body after eliminating the influence of shale and siliceous lithology includes:

[0031] The threshold values ​​of mud content and silica content are used as conditions, and the wave impedance data body after eliminating the influence of mud and silica lithology is obtained through conditional selection logic operation; the threshold values ​​of mud content and silica content are obtained by intersecting the mud content and silica content curves calculated by drilling with the vertical position of the sweet spot reservoir on the drilling.

[0032] According to the method of the present invention, preferably, natural gamma ray inversion is used to remove non-reservoir layers with high shale content and natural gamma greater than 20 API; and silica content inversion is used to remove lithology with silica content greater than 20%. The two threshold values ​​may be slightly different for different target areas.

[0033] According to the method of the present invention, preferably, the relationship between the wave impedance and the porosity after eliminating the influence of lithology is obtained by linear fitting. It is understood by those skilled in the art that the coefficients in the fitting relationship obtained in different target areas are different.

[0034] According to the method of the present invention, preferably, the sweet spot reservoir porosity standard is established based on the relationship between reservoir porosity and single well productivity. The higher the single well productivity, the greater the porosity. For example, in an embodiment of the present invention, the sweet spot reservoir porosity standard is porosity>3.2%.

[0035] Compared with the previous methods of predicting reservoir distribution through karst paleo-geomorphology restoration and reservoir thickness prediction simply through reservoir inversion, the prediction method of the present invention utilizes new geological knowledge and fully considers the three aspects of the main controlling factors of microbial rocks (lithophyte, karstification and rock fabric). It proposes a new idea of ​​sweet spot reservoir prediction with step-by-step control of "paleo-geomorphology restoration zoning based on well-seismic constraints, seismic attribute analysis based on forward simulation, and reservoir physical property inversion point selection based on mud and silicon removal". In particular, it carries out porosity prediction, solves the influence of different microbial rock fabrics on the quality of reservoirs, and greatly improves the accuracy of reservoir prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1The present invention is a schematic flow chart of a method for predicting a microbial rock sweet spot reservoir.

[0037] Figure 2 This is the paleomorphic map of the upper sub-section of Deng Si of Dengying Formation in GM area in the embodiment.

[0038] Figure 3 It is the RMS amplitude plane diagram of the upper sub-segment of the lamp in the GM area lamp shadow group in the embodiment.

[0039] Figure 4 This is the forward modeling diagram of the reservoir of the upper sub-member of Deng 4 of the Dengying Formation in the GM area in the embodiment.

[0040] Figure 5 This is the seismic attribute fusion map of the upper subsegment of Deng 4 of the Dengying Formation in the GM area in the embodiment.

[0041] Figure 6 This is a relationship diagram between wave impedance and porosity of the upper sub-segment of Dengying Formation Deng 4 in the GM area in the embodiment (after eliminating the influence of lithology).

[0042] Figure 7 This is the porosity inversion section of the reservoir through GS10-GS18 in the GM area in the embodiment (after removing mud and silica).

[0043] Figure 8 This is the distribution and structural superposition of the sweet spot reservoir in the upper sub-member of Dengying Formation Deng 4 in the GM area in the embodiment. DETAILED DESCRIPTION

[0044] In order to explain the present invention more clearly, the present invention is further described below in conjunction with preferred embodiments. It should be understood by those skilled in the art that the following specific description is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0045] The present invention takes into account the three main controlling factors of microbial rocks (lithophyte, karstification and rock fabric) by using new geological knowledge, and proposes a new concept of sweet spot reservoir prediction with step-by-step control of "paleomorphological restoration and zoning based on well-seismic constraints, seismic attribute analysis and zoning based on forward simulation, and reservoir physical property inversion and point selection based on mud and silicon removal" (such as Figure 1 In particular, porosity prediction was carried out to address the impact of different microbial rock structures on the quality of reservoirs, greatly improving the accuracy of reservoir prediction.

[0046] like Figure 1 As shown, the method for predicting the microbial rock sweet spot reservoir of the present invention includes three levels of control, including:

[0047] S100. Use paleo-geomorphology restoration technology based on well-seismic constraints to divide the microbial reservoir into sedimentary facies zones.

[0048] Specifically: on the basis of single well sedimentary facies analysis, well-seismic calibration is performed to clarify the paleo-geomorphic features and seismic reflection characteristics of different phase belts; through paleo-geomorphic restoration, combined with seismic attribute analysis and well-seismic constraints, the sedimentary facies distribution is divided (i.e., sedimentary facies belt prediction).

[0049] S200. Determine reservoir distribution using seismic attribute analysis technology based on forward modeling.

[0050] Specifically, the reservoir seismic response model is established through well-seismic calibration; the reservoir development model corresponding to different seismic response characteristics is clarified based on forward simulation; sensitive seismic attributes are optimized, and multi-attribute fusion is performed to determine the distribution of favorable reservoir development areas (i.e. reservoir distribution prediction).

[0051] S300. Optimize sweet spot reservoirs by using reservoir property inversion technology based on desludging and desiliconization.

[0052] Specifically, first, the inversion method is optimized; second, wave impedance inversion and natural gamma and silica content inversion are carried out; then, the three sets of inversion data are used for calculation to obtain the wave impedance data after eliminating the influence of mud and silica. According to the relationship between wave impedance and porosity after eliminating the influence of lithology, the wave impedance data after eliminating the influence of lithology is converted into porosity data; finally, according to the sweet spot reservoir porosity standard, the sweet spot reservoir development area (i.e. reservoir sweet spot prediction) is optimized.

[0053] The natural gamma inversion is Figure 1 The desludging inversion in the siliceous content inversion is Figure 1 Desiliconization inversion.

[0054] The embodiment of the present invention uses the method of the present invention to predict the microbial rock sweet spot reservoir in the Sinian Dengying Formation reservoir in the GM area of ​​the SC basin.

[0055] In the GM area of ​​the SC Basin, the Sinian Dengying Formation reservoirs are widely developed, with strong heterogeneity and poor overall reservoir properties, but there are still sweet spot reservoirs.

[0056] In this specific application, the specific process is as follows:

[0057] (1) The sedimentary facies zones of the microbial reservoir were divided using paleogeomorphological restoration technology based on well-seismic constraints.

[0058] Based on the data of cores, thin sections, logging, drilling and testing, a single well sedimentary facies analysis was conducted, and it was believed that microbial rocks were generally developed in the upper sub-member of Dengying Formation Deng 4, and carbonate platform facies were the main sediments. The platform margin was dominated by mound-shoal facies, and the platform was dominated by platform flat facies, with mound-shoal facies developed locally. According to drilling statistics, the degree of reservoir development is closely related to the sedimentary facies, and the mound-shoal facies is most conducive to reservoir development. In terms of porosity, the clotted dolomite in the mound-shoal facies is greater than the stromatolite and laminated dolomite.

[0059] Through well-seismic calibration, the seismic reflection characteristics and paleo-geomorphic characteristics of the platform margin and the platform of the upper sub-member of Dengying Formation Deng 4 are clarified. According to the calibration results, the platform margin presents the seismic response characteristics of intermittent or blank reflection and weak amplitude energy; the platform presents the seismic response characteristics of continuous reflection and strong amplitude energy. On the whole, the platform margin has thicker strata and higher paleo-geomorphology than the platform, but there is still a single small-scale hilly beach phase in the platform, and the paleo-geomorphology is relatively high locally.

[0060] Using the residual stratigraphic thickness method, the paleo-geomorphology of the Dengying Formation was restored by calculating the time difference between the bottom of the upper Deng 4th submember and the bottom of the Cambrian. Figure 2 According to the seismic response characteristics of different phase belts, the root mean square amplitude attribute is selected, and the root mean square amplitude attribute is extracted 15ms down along the bottom boundary of the Cambrian, as shown in Figure 3 As shown. According to the above restored ancient landforms ( Figure 2 ) and extracted seismic attributes ( Figure 3 ) results, the single well sedimentary facies was used to constrain the distribution of microbial reservoirs at the platform margin and within the platform.

[0061] (2) Determine reservoir distribution using seismic attribute analysis technology based on forward modeling.

[0062] Through well seismic calibration, reservoir seismic response modes are established: "wide trough + double peaks", "wide trough", "wide trough + single peak", and the relationship between different seismic response modes and production capacity is established: the "wide trough + double peaks" response mode is most likely to be high-yield.

[0063] Establish geological models with different reservoir thicknesses and reservoir development degrees above and below the siliceous layer to carry out forward simulations and clarify the reservoir development patterns corresponding to different seismic response characteristics, such as Figure 4 shown.

[0064] According to the seismic response characteristics of the three reservoir development modes, the neural network algorithm is used to select sensitive seismic attributes, extract the corresponding sensitive attributes, and then perform seismic attribute fusion (such as Figure 5 to determine the reservoir distribution.

[0065] (3) Utilize reservoir property inversion technology based on desludging and desiliconization to optimize sweet spot reservoirs.

[0066] From the intersection of the wave impedance of the drilled wells and the porosity interpreted by logging, there is a certain correlation between the wave impedance and porosity of the upper sub-section of the Deng 4th Member, but the range is wide, and it is difficult to use a single linear relationship to calculate the porosity data body of the Dengying Formation reservoir. The reason is that the reservoir and the mud-bearing and siliceous dolomite all show low values ​​on the acoustic wave curve. From the intersection of natural gamma and core porosity, it can be seen that the natural gamma of the reservoir is less than 20API, so the natural gamma inversion can be used to remove non-reservoir layers with high mud content. From the intersection of silica content and core porosity, it can be seen that when the silica content is greater than 20%, it is a non-reservoir layer, so the silica content inversion can be used to remove the lithology with a silica content greater than 20%. Establish the relationship between wave impedance and porosity after eliminating the influence of mud and siliceous lithology (such as Figure 6 As shown in the figure, the fitting formula is: y(porosity)=-417.42x(wave impedance)+18990, so this relationship can be used to quantitatively predict the reservoir porosity.

[0067] Specific steps: First, test different inversion methods, and select the waveform indication inversion method suitable for the area for inversion to obtain the original wave impedance inversion data volume; second, carry out natural gamma and silica content inversion to obtain two sets of lithology inversion data volumes, and calculate these two sets of lithology data volumes with the wave impedance data volume to obtain the wave impedance data volume after eliminating the lithology effect; then, according to the relationship between wave impedance and porosity after eliminating the lithology effect, the wave impedance data volume after eliminating the lithology effect is converted into a porosity inversion data volume ( Figure 7 ); Finally, based on the relationship between reservoir porosity and single well productivity, the sweet spot reservoir standard (porosity>3.2%) was established, and the sweet spot reservoir development area was selected ( Figure 8 ).

[0068] Using the above new method of predicting sweet spot reservoirs with step-by-step control, 7 sweet spot reservoir development areas with a total area of ​​927.63 km were identified in the platform margin area. 2 ; 17 sweet spot reservoir development areas were identified in the inner area of ​​the platform, totaling 1312.04 km 2 Ten wells were deployed in the sweet reservoir development area predicted based on this, and all of them encountered sweet reservoirs and obtained high-yield gas flow.

[0069] From the perspective of exploration results, the sweet spot reservoir development area identified by the step-by-step controlled sweet spot reservoir prediction technology of the present invention is consistent with the actual drilling results. Practice shows that this technology is feasible for predicting microbial rock sweet spot reservoirs and is worthy of reference and application in similar areas.

[0070] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the protection scope of the present invention.

Claims

1. A method for predicting microbial rock sweet spot reservoirs, characterized in that: The following steps are involved: S100. Use paleo-geomorphological restoration technology based on well-seismic constraints to divide the microbial reservoir into sedimentary facies zones to determine favorable facies zones; Specifically include: Based on the single well sedimentary phase analysis, well-seismic calibration is performed to clarify the paleo-geomorphic features and seismic reflection features of different sedimentary phase belts; through paleo-geomorphic restoration, combined with seismic attribute analysis and well-seismic constraints, the microbial rock reservoir is divided into sedimentary phase belts; wherein, the paleo-geomorphic restoration is performed using the residual stratigraphic thickness method; S200, within the favorable phase zone, using seismic attribute analysis technology based on forward simulation to determine reservoir distribution to determine reservoir development areas; specifically including: Through well seismic calibration, reservoir seismic response modes are established: "wide trough + double peaks", "wide trough", "wide trough + single peak", and the relationship between different seismic response modes and production capacity is established; geological models with different reservoir thicknesses and reservoir development levels above and below the siliceous layer are established to carry out forward simulation and clarify the reservoir development modes corresponding to different seismic response characteristics; based on the seismic response characteristics of the three reservoir development modes, the neural network algorithm is used to select sensitive seismic attributes, extract the corresponding sensitive attributes respectively, and then perform seismic attribute fusion to determine the distribution of favorable reservoir development areas; S300, in the reservoir development area, using reservoir physical property inversion technology based on desludging and desiliconization to determine the sweet spot reservoir; specifically comprising: First, an inversion method is selected; second, wave impedance inversion and natural gamma and siliceous content inversion are carried out; then, the three sets of inversion data bodies are used for calculation to obtain the wave impedance data body after eliminating the influence of mud and siliceous lithology; according to the relationship between wave impedance and porosity after eliminating the influence of lithology, the wave impedance data body after eliminating the influence of lithology is converted into a porosity data body; finally, according to the sweet spot reservoir porosity standard, the sweet spot reservoir development area is determined; the sweet spot reservoir porosity standard is established according to the relationship between reservoir porosity and single well production capacity.

2. The method according to claim 1, characterized in that The favorable phase belt described in S100 is the hill-beach phase.

3. The method according to claim 1, characterized in that The calculation process of the wave impedance data body after eliminating the influence of shale and siliceous lithology includes: The threshold values ​​of mud content and silica content are used as conditions, and the wave impedance data body after eliminating the influence of mud and silica lithology is obtained through conditional selection logic operation; the threshold values ​​of mud content and silica content are obtained by intersecting the mud content and silica content curves calculated by drilling with the vertical position of the sweet spot reservoir on the drilling.

4. The method according to claim 3, characterized in that Use natural gamma ray inversion to remove non-reservoir layers with high shale content and natural gamma greater than 20 API; Silica content inversion is used to remove lithologies with silica content greater than 20%.

5. The method according to claim 1, characterized in that The relationship between the wave impedance and the porosity after eliminating the influence of lithology is obtained by linear fitting.

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