Method, device and electronic equipment for determining productivity of low-resistance oil reservoir

Abstract

The present invention discloses a method, device and electronic equipment for determining the productivity of a low-resistivity reservoir. The method includes: obtaining the liquid production ratio data and resistance value distribution data of the low-resistivity reservoir in a reference well; determining a first regression function corresponding to the reference well based on the liquid production ratio data and the resistance value distribution data, and determining the predicted liquid production ratio data of the low-resistivity reservoir in the reference well based on the first regression function and the resistance value distribution data; determining a second regression function corresponding to the reference well based on the predicted liquid production ratio data, the liquid production ratio data and the first regression function; obtaining the predicted target liquid production ratio data of the low-resistivity reservoir in the target well based on the second regression function and the target resistance value distribution data of the low-resistivity reservoir in the target well, so as to determine the productivity of the low-resistivity reservoir in the target well based on the predicted target liquid production ratio data. The technical solution of the embodiment of the present invention reduces the dependence on high-end logging, and can achieve accurate prediction of the productivity of complex low-resistivity reservoirs based on conventional logging data.

Description

Technical Field

[0001] The present invention relates to the technical field of oil exploration, and particularly to a method, device and electronic equipment for determining the productivity of low-resistivity reservoirs. Background Art

[0002] Due to the unique microscopic reservoir characteristics, low-resistivity reservoirs have low resistivity and are difficult to identify using conventional logging data, belonging to a kind of hidden reservoir. In recent years, with the progress of logging identification technology and the increasing attention in the industry, new discoveries of low-resistivity reservoirs have been continuously made, having great reserve potential and development potential. However, due to the low resistivity of low-resistivity reservoirs, when using conventional logging data (natural gamma, resistivity) for logging interpretation, the interpretation conclusions of reservoir physical properties have great uncertainty, thus leading to difficult productivity prediction and large differences in single-well productivity. In addition, with the water injection development of old oilfields, low-resistivity reservoirs have been flooded to varying degrees, further leading to the complexity of the resistivity structure of low-resistivity reservoirs, increasing the difficulty and uncertainty of their logging interpretation, and bringing great risks to the productivity construction of oilfields.

[0003] At present, the research on the productivity prediction of low-resistivity reservoirs is relatively less, and mainly focuses on how to use high-end logging such as nuclear magnetic resonance to improve the logging interpretation accuracy of low-resistivity reservoirs, and then improve the productivity prediction accuracy. This method is too dependent on high-end logging, with high costs and cannot be widely applied in the daily production of oilfields. Especially for offshore oilfields, considering the engineering implementation safety and economy, high-end logging data is extremely scarce, and the logging data of most wells only includes natural gamma and resistivity. Therefore, the existing productivity prediction methods for low-resistivity reservoirs have great limitations. Summary of the Invention

[0004] The present invention provides a method, device and electronic equipment for determining the productivity of low-resistivity reservoirs, so as to reduce the dependence on high-end logging and achieve accurate prediction of the productivity of complex low-resistivity reservoirs based on conventional logging data.

[0005] According to one aspect of the present invention, a method for determining the productivity of a low-resistivity reservoir is provided, including:

[0006] Obtaining the liquid production ratio data and resistance value distribution data of the low-resistivity reservoir in a reference well; wherein, the resistance value distribution data includes the thickness ratio corresponding to low-resistivity reservoirs with different resistance values;

[0007] Determining a first regression function corresponding to the reference well based on the liquid production ratio data and the resistance value distribution data, and determining the predicted liquid production ratio data of the low-resistivity reservoir in the reference well based on the first regression function and the resistance value distribution data;

[0008] Determine the second regression function corresponding to the reference well based on the specific liquid production prediction data, the specific liquid production data, and the first regression function;

[0009] Based on the second regression function and the target resistance value distribution data of the low-resistivity reservoir in the target well, obtain the target specific liquid production prediction data of the low-resistivity reservoir in the target well, so as to determine the productivity of the low-resistivity reservoir in the target well based on the target specific liquid production prediction data.

[0010] According to another aspect of the present invention, there is provided a device for determining the productivity of a low-resistivity reservoir, including:

[0011] A data acquisition module for acquiring the specific liquid production data and the resistance value distribution data of the low-resistivity reservoir in the reference well; wherein, the resistance value distribution data includes the thickness ratio corresponding to the low-resistivity reservoir with different resistance values;

[0012] A specific liquid production prediction module for determining the first regression function corresponding to the reference well based on the specific liquid production data and the resistance value distribution data, and determining the specific liquid production prediction data of the low-resistivity reservoir in the reference well based on the first regression function and the resistance value distribution data;

[0013] A regression function determination module for determining the second regression function corresponding to the reference well based on the specific liquid production prediction data, the specific liquid production data, and the first regression function;

[0014] A productivity determination module for obtaining the target specific liquid production prediction data of the low-resistivity reservoir in the target well based on the second regression function and the target resistance value distribution data of the low-resistivity reservoir in the target well, so as to determine the productivity of the low-resistivity reservoir in the target well based on the target specific liquid production prediction data.

[0015] According to another aspect of the present invention, there is provided an electronic device, the electronic device includes:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining the productivity of a low-resistivity reservoir according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, there is provided a computer-readable storage medium, the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a processor to execute the method for determining the productivity of a low-resistivity reservoir according to any embodiment of the present invention when executed.

[0020] In the technical solution of the embodiment of the present invention, by obtaining the liquid production ratio data and resistance value distribution data of the low-resistivity reservoir in the reference well; determining the first regression function corresponding to the reference well based on the liquid production ratio data and the resistance value distribution data, and determining the predicted liquid production ratio data of the low-resistivity reservoir in the reference well based on the first regression function and the resistance value distribution data; determining the second regression function corresponding to the reference well based on the predicted liquid production ratio data, the liquid production ratio data, and the first regression function; and obtaining the predicted target liquid production ratio data of the low-resistivity reservoir in the target well based on the second regression function and the target resistance value distribution data of the low-resistivity reservoir in the target well, so as to determine the productivity of the low-resistivity reservoir in the target well based on the predicted target liquid production ratio data. The technical solution of the embodiment of the present invention solves the problem that the existing technical methods mainly rely on high-end logging, with high costs and unable to be widely applied in the daily production of oilfields, and realizes reducing the dependence on high-end logging and enabling accurate prediction of the productivity of complex low-resistivity reservoirs based on conventional logging data.

[0021] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understandable through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0023] Figure 1 FIG.

[0024] Figure 2 is a flowchart of a method for determining the productivity of a low-resistivity reservoir provided in Embodiment 1 of the present invention;

[0025] Figure 3 is a sedimentary microfacies map of the low-resistivity reservoir in the target work area of Embodiment 2 of the present invention;

[0026] Figure 4 is a grain size probability curve diagram of the main braided stream belt of the target reservoir in Embodiment 2 of the present invention;

[0027] Figure 5 is a grain size probability curve diagram of the secondary braided stream belt of the target reservoir in Embodiment 2 of the present invention;

[0028] Figure 6 is a relationship diagram between the median particle size and the clay mineral content of the target reservoir in Embodiment 2 of the present invention;

[0029] Figure 7 It is the relationship diagram between the resistivity of the target reservoir and the montmorillonite whole-rock content in the second embodiment of the present invention;

[0030] Figure 8 It is the probability histogram of the resistivity distribution of the target reservoir in the second embodiment of the present invention;

[0031] Figure 9 It is the relationship diagram between the liquid production index ratio and the thickness ratio of 0 - 6Ω·m provided in the second embodiment of the present invention;

[0032] Figure 10 It is the relationship diagram between the liquid production index ratio and the thickness ratio of 6 - 8Ω·m provided in the second embodiment of the present invention;

[0033] Figure 11 It is the relationship diagram between the liquid production index ratio and the thickness ratio of 8 - 10Ω·m provided in the second embodiment of the present invention;

[0034] Figure 12 It is the relationship diagram between the liquid production index ratio and the thickness ratio of >10Ω·m provided in the second embodiment of the present invention;

[0035] Figure 13 It is the isogram of the liquid production index ratio of the low-resistivity oil reservoir in the target work area in the second embodiment of the present invention;

[0036] Figure 14 It is the isogram of the liquid production index of the low-resistivity oil reservoir in the target work area in the second embodiment of the present invention;

[0037] Figure 15 It is the structural schematic diagram of a device for determining the productivity of a low-resistivity oil reservoir provided in the third embodiment of the present invention;

[0038] Figure 16 It shows the structural schematic diagram of an electronic device that can be used to implement the embodiments of the present invention. Detailed implementation manners

[0039] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0040] It should be noted that the terms "first", "second", etc. in the description, claims and the above-mentioned drawings of the present invention are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0041] Embodiment 1

[0042] Figure 1 The flowchart of a method for determining the productivity of a low-resistivity reservoir provided in Embodiment 1 of the present invention is applicable to the situation of predicting the productivity of a low-resistivity reservoir in a drilling well and providing a basis for the drilling well according to the prediction result. This method can be executed by a device for determining the productivity of a low-resistivity reservoir, which can be implemented in the form of hardware and / or software, and can be configured in a computer device. As Figure 1 shown, the method includes:

[0043] S110. Obtain the liquid production ratio data and resistance value distribution data of the low-resistivity reservoir in the reference drilling well.

[0044] Among them, the reference drilling well refers to an oil drilling well used as a research reference, and a low-resistivity reservoir with a low resistivity is included in this kind of oil drilling well; the liquid production ratio data represents the daily liquid production per meter of effective thickness increased when the production pressure difference increases by 1 MPa during the exploitation of the low-resistivity reservoir in the reference drilling well. For example, the liquid production ratio data is the liquid production ratio index of the low-resistivity reservoir in the reference drilling well; the resistance value distribution data refers to the resistance value distribution situation of the low-resistivity reservoir in the reference drilling well. For example, in the low-resistivity reservoir of the reference drilling well, the proportion X of the thickness of the low-resistivity reservoir with a resistance value of A in the total thickness of the low-resistivity reservoir, and the proportion of the thickness of the low-resistivity reservoir with a resistance value of B in the total thickness of the low-resistivity reservoir is Y.

[0045] It can be understood that the liquid production ratio data of the low-resistivity reservoir represents the productivity of the low-resistivity reservoir to a certain extent. Therefore, when studying the low-resistivity reservoir of the reference drilling well, it is necessary to first obtain the liquid production ratio data and resistance value distribution data of the low-resistivity reservoir in the reference drilling well. For example, some logging instruments are used to collect the parameters related to the liquid production ratio data and resistance value distribution data in the reference drilling well, and then the collected parameters are processed or calculated to obtain the liquid production ratio data and resistance value distribution data of the reference drilling well.

[0046] In this embodiment, the obtaining of the liquid production ratio data of the low-resistivity oil reservoir in the reference well includes: taking the low-resistivity oil reservoir wells in the preset area that have not been flooded and have undergone liquid production profile testing as the reference wells, and obtaining the daily production, thickness value, and pressure value of the low-resistivity oil reservoir in the reference wells; calculating the liquid production ratio data of the low-resistivity oil reservoir in the reference wells based on the daily production, the thickness value, and the pressure value.

[0047] Among them, the preset area can be a geographical area pre-selected by the user. In this geographical area, multiple wells including low-resistivity oil reservoirs are selected as reference wells. It should be noted that the reference wells are wells that have undergone liquid production profile testing and have not been flooded. Because only the wells that have undergone liquid production profile testing can obtain the parameters in the reference wells, such as thickness parameters, pressure parameters, daily production parameters, etc.; if the well is flooded, it will cause the resistivity structure of the low-resistivity oil reservoir to become complex, increasing the difficulty and uncertainty of its logging interpretation, and bringing greater risks to the oilfield production capacity construction. The daily production parameter refers to the daily liquid production of the low-resistivity oil reservoir in the reference well. The daily liquid production is represented by L, and the corresponding unit is "m³ / day". The thickness value refers to the thickness data of the low-resistivity oil reservoir, represented by H, and the corresponding unit can be "m"; the pressure value refers to the production pressure difference of the low-resistivity oil reservoir.

[0048] In a preferred embodiment, the daily production data is collected by a logging instrument, and at the same time, the corresponding thickness data of the low-resistivity oil reservoir is collected; the pressure value of the reference well can be obtained from the formation pressure and the bottom-hole flowing pressure corresponding to the low-resistivity oil reservoir in the well. Further, the liquid production index corresponding to the low-resistivity oil reservoir in the reference well is calculated according to formula (1):

[0049] Y = L / [(P1 - P2) * H] (1)

[0050] Among them, Y is the liquid production index, P1 is the formation pressure, and P2 is the bottom-hole flowing pressure.

[0051] The determination process of the formation pressure and the bottom-hole pressure is introduced as follows:

[0052] The most direct and accurate way to obtain the formation pressure (P1, MPa) is to conduct MDT pressure tests. However, since most PLT tests are mainly applied to old wells, and MDT pressure measurements cannot be carried out on old wells. Therefore, to obtain the formation pressure of low-resistivity reservoirs during PLT tests on old wells, the following two methods can be used: The first method: Borrow the MDT pressure test data of low-resistivity reservoirs in nearby new wells. According to reservoir development experience, generally, the formation pressure can be considered stable in the short term (0 - 2 months). Take the 0 - 2 months before and after the PLT test as the pressure stability time window. During the acquisition time window, obtain the MDT pressure measurement data of low-resistivity reservoirs in new wells near the reference well, and borrow and use it as the formation pressure (P1, MPa) during the PLT test. The second method: Calculate the formation static pressure. In well areas with longitudinal formation pressure equilibrium and no reservoir pollution, use the formation static pressure data of this well or nearby adjacent wells within 0 - 2 months before and after the PLT test to calculate the formation pressure (P1, MPa) of the low-resistivity reservoir.

[0053] For the bottom-hole pressure, collect the pressure monitoring data (PJ, MPa) of the reference well, and calculate the bottom-hole flowing pressure (P2, MPa) according to the difference in pump setting depth and the burial depth of the low-resistivity reservoir (HJ, m). The pressure monitoring data are conventional data in oilfield production and come from the pressure monitor at the pump setting position.

[0054] Based on the above solution, obtaining the resistivity distribution data of the low-resistivity reservoir in the reference well includes: dividing the resistivity range corresponding to the low-resistivity reservoir in the reference well into multiple resistivity intervals based on the resistivity range and interval division conditions corresponding to the low-resistivity reservoir in the reference well; determining the thickness ratio of the low-resistivity reservoir corresponding to each resistivity interval in the reference well, and taking the thickness ratios of the low-resistivity reservoirs corresponding to each resistivity interval as the resistivity distribution data.

[0055] Among them, the resistivity range refers to the maximum and minimum values of the corresponding resistivity of the low-resistivity reservoir. For example, in a certain reference well, the maximum resistivity of the low-resistivity reservoir is 10 ohms and the minimum is 0 ohms, and its corresponding resistivity range is [0, 10]; the division condition refers to how many intervals the resistivity range is divided into; correspondingly, determine the thickness ratio data of the low-resistivity reservoir within different resistivity intervals. Exemplarily, divide the resistivity range into n resistivity intervals, respectively count the total thickness H (m) of the low-resistivity reservoir in the reference well, and the thicknesses h1...hn (m) of the low-resistivity reservoir in each resistivity interval, and calculate the thickness ratios X1...Xn (%) corresponding to each interval of the low-resistivity reservoir in the reference well based on the thicknesses of the low-resistivity reservoirs in each resistivity interval and the total thickness, so as to take the thickness ratios as the resistivity distribution data of the reference well. The above content introduces the processing method for one of the reference wells, and the processing methods for other reference wells are similar.

[0056] S120. Determine the first regression function corresponding to the reference well based on the specific liquid production data and the resistance value distribution data, and determine the predicted specific liquid production data of the low-resistivity reservoir in the reference well based on the first regression function and the resistance value distribution data.

[0057] In this embodiment, the specific liquid production index of each reference well and the thickness ratio corresponding to different resistance value intervals in each reference well can be obtained. Further, a relationship regression is performed between the specific liquid production index and the thickness ratio to obtain the corresponding regression formula, which is the first regression function. In the first regression function, the functional relationship between the thickness ratio and the specific liquid production index is reflected. Therefore, based on the first regression function and the resistance value distribution data of the reference well, the specific liquid production index of the low-resistivity reservoir in the reference well can be predicted to obtain the predicted specific liquid production data.

[0058] Based on the above solution, the step of determining the first regression function corresponding to the reference well based on the specific liquid production data and the resistance value distribution data includes: performing a relationship regression calculation based on the specific liquid production data and the resistance value distribution data to obtain the first regression function corresponding to the reference well.

[0059] In an embodiment of the present invention, the step of determining the predicted specific liquid production data of the low-resistivity reservoir in the reference well based on the first regression function and the resistance value distribution data includes: substituting the resistance value distribution data into the first regression function for calculation to obtain a calculation result corresponding to the resistance value distribution data, and using the calculation result as the predicted specific liquid production data corresponding to the low-resistivity reservoir in the reference well.

[0060] Specifically, the thickness ratios corresponding to each resistance value interval of the reference well can be substituted into the first regression function, and the obtained result is the predicted specific liquid production data of the low-resistivity reservoir in the reference well.

[0061] S130. Determine the second regression function corresponding to the reference well based on the predicted specific liquid production data, the specific liquid production data, and the first regression function.

[0062] Based on the above solution, the step of determining the second regression function corresponding to the reference well based on the predicted specific liquid production data, the specific liquid production data, and the first regression function includes: performing a regression analysis based on the specific liquid production data and the predicted specific liquid production data of the low-resistivity reservoir in the reference well to obtain a second regression function to be processed; obtaining the second regression function corresponding to the reference well based on the first regression function and the second regression function to be processed.

[0063] It can be understood that there are differences between the liquid production ratio data and the predicted liquid production ratio data. At this time, the predicted liquid production ratio data can be used as the independent variable, and the liquid production ratio data can be used as the dependent variable for relationship regression. Finally, a regression function to be used is obtained, which represents the functional relationship between the liquid production ratio data and the predicted liquid production ratio data; in the first regression function, it represents the functional relationship between the thickness ratio and the liquid production index; the second regression function of the reference well can be obtained by combining the first regression function and the regression function to be used.

[0064] In a preferred embodiment, before step S140, it may further include: determining the corresponding existing resistance distribution data of the low-resistivity oil reservoirs in multiple existing wells, and obtaining the to-be-used liquid production ratio data of each of the existing wells based on the existing resistance distribution data and the resistance second regression function; on the basis of sedimentary facies control, drawing a liquid production ratio isoline map and a liquid production index isoline map corresponding to the multiple existing wells based on the to-be-used liquid production ratio data of each well; based on the liquid production isoline map and the liquid production index isoline map, selecting the well location of the target well before drilling.

[0065] Among them, the existing wells refer to all the wells that have not been flooded, and the existing wells include the wells that cannot obtain the liquid production ratio data and the existing wells that can obtain the liquid production ratio data; the existing resistance distribution data refers to the thickness ratio of the low-resistivity oil reservoirs corresponding to each resistance interval in the existing wells. The to-be-used liquid production ratio data can be understood as the predicted liquid production ratio data of the existing wells.

[0066] It can be understood that among the existing wells that have not been flooded, some are the existing wells that can obtain the liquid production index, and some are the existing wells that cannot obtain the liquid production index. For the existing wells that cannot obtain the liquid production index, the corresponding existing resistance distribution data can be substituted into the second regression function, and the obtained value can be used as the to-be-used liquid production ratio data of the existing wells. That is, for some existing wells that cannot obtain the liquid production index, the to-be-used liquid production ratio data of the existing wells can be predicted through the existing resistance distribution data and the second regression function.

[0067] After obtaining the specific liquid production indices of multiple existing wells through the above process, the corresponding liquid production indices can be calculated, and then, under the control of the sedimentary microfacies map, the isochore maps of the specific liquid production index and the liquid production index of the entire oilfield can be drawn. Based on the isochore maps of the specific liquid production index and the liquid production index of the entire oilfield, during the pre-drilling stage or well location selection stage of a new well, the productivity prediction data of the low-resistivity reservoir can be read on the plan view according to the projection of the well location coordinates. For example, when drilling is to be carried out at location A, first, through the liquid production index isochore map, find the liquid production index corresponding to location A on the isochore map. If the liquid production index is high, then location A can be used as the target drilling location. If the liquid production index of location A is low, then location A cannot be used as the target drilling location.

[0068] S140. Based on the second regression function and the target resistance value distribution data of the low-resistivity reservoir in the target well, obtain the target specific liquid production prediction data of the low-resistivity reservoir in the target well, so as to determine the productivity of the low-resistivity reservoir in the target well based on the target specific liquid production prediction data.

[0069] Among them, the target well refers to the low-resistivity reservoir well for which the productivity needs to be predicted, and the target resistance value distribution data refers to the thickness ratio corresponding to each resistance value interval of the low-resistivity reservoir in the target well. The target specific liquid production prediction data refers to the specific liquid production index of the low-resistivity reservoir in the target well, and this index is determined by the second regression function and is not obtained through the liquid production profile test.

[0070] In practical applications, after drilling, in order to conduct dynamic analysis on the target well, if the target well has not been flooded, the productivity of the low-resistivity reservoir in the target well can be dynamically predicted according to the target resistance value distribution data and the second regression function of the target well. Correspondingly, if the target well has been flooded, the specific liquid production index and the liquid production index can be directly read on the plan view with reference to the isochore maps of the specific liquid production index and the liquid production index of the entire oilfield for the productivity analysis of the target well.

[0071] In the embodiment of the present invention, the step of obtaining the target specific liquid production prediction data of the low-resistivity reservoir in the target well based on the second regression function and the target resistance value distribution data of the low-resistivity reservoir in the target well includes: substituting the target resistance value distribution data of the low-resistivity reservoir in the target well into the second regression function to obtain the target specific liquid production prediction data of the low-resistivity reservoir in the target well.

[0072] Specifically, substituting the resistance value distribution data of the target well into the second regression function, and taking the obtained result as the specific liquid production prediction data of the low-resistivity reservoir in the target well, and then calculating the liquid production index J = Y * H. According to the calculated specific liquid production index and the liquid production index data.

[0073] The technical solution of the embodiment of the present invention includes: obtaining the liquid production ratio data and resistance value distribution data of the low-resistivity reservoir in the reference well; determining the first regression function corresponding to the reference well based on the liquid production ratio data and the resistance value distribution data, and determining the predicted liquid production ratio data of the low-resistivity reservoir in the reference well based on the first regression function and the resistance value distribution data; determining the second regression function corresponding to the reference well based on the predicted liquid production ratio data, the liquid production ratio data and the first regression function; and obtaining the target predicted liquid production ratio data of the low-resistivity reservoir in the target well based on the second regression function and the target resistance value distribution data of the low-resistivity reservoir in the target well, so as to determine the productivity of the low-resistivity reservoir in the target well based on the target predicted liquid production ratio data. The technical solution of the embodiment of the present invention solves the problem that the existing technical methods mainly rely on high-end logging, with high costs and cannot be widely applied in the daily production of oil fields, and realizes reducing the dependence on high-end logging and accurately predicting the productivity of complex low-resistivity reservoirs based on conventional logging data.

[0074] Embodiment 2

[0075] Figure 2 FIG. is a flowchart of a method for determining the productivity of a low-resistivity reservoir provided in Embodiment 2 of the present invention, and this embodiment is a preferred embodiment of the above embodiment. As Figure 2 shown, the method includes:

[0076] S210. Determine the genetic type of the low-resistivity reservoir and the reservoir sedimentary characteristics.

[0077] Comprehensively utilize core experiment data to analyze the microscopic characteristics of the reservoir and clarify the genetic type of the low-resistivity reservoir; then comprehensively utilize paleoclimate information, cores, wall cores, thin sections, grain size probability maps, logging, seismic and other data to clarify the planar distribution of sedimentary microfacies of the reservoir, hydrodynamic conditions, sedimentation mechanisms, etc.

[0078] By analyzing the core experiment data of the target low-resistivity reservoir, it is considered that the reservoir of this oil reservoir has characteristics such as a large grain size distribution range, poor sorting, high shale content, and high content of clay minerals such as montmorillonite. The additional conductive effect of clay minerals such as montmorillonite is the main reason for the low resistivity of this oil reservoir. At the same time, due to the poor sorting of the reservoir, complex pore structure, and high irreducible water saturation, the resistivity of the oil reservoir is further reduced. Finally, it is clarified that this low-resistivity reservoir is a low-resistivity reservoir with a composite origin of high irreducible water - clay additional conductivity.

[0079] During the sedimentation period of this oil reservoir, the paleoclimate was cold and arid, and the hydrodynamic force changed greatly with seasons. The sedimentary subfacies can be subdivided into the main braided stream belt and the secondary braided stream belt. The main braided stream belt has two branches in the north and south. One branch is located on the north side of the study area and extends in the east-west direction, and the other branch is located on the southeast side of the study area and extends in the northeast-southwest direction. The main braided stream belt can be subdivided into the channel bar microfacies and the channel microfacies; the secondary braided stream belt mainly relies on multiple small-scale braided stream belts extending southwestward from the north branch of the main braided stream belt, such as Figure 3 shown.Figure 3 It is the sedimentary microfacies map of the low-resistivity oil reservoir in the target work area of the second embodiment of the present invention.

[0080] The main braided stream belt is the main water passage. It can be seen from the core and mudstone color that the main braided stream belt is mainly in reducing colors such as gray and grayish green as a whole, indicating an underwater environment, showing that even in the dry season, a certain water level is still maintained in the main braided stream belt. Through the grain size probability distribution map (as Figure 4 shown, Figure 4 which is the grain size probability curve of the main braided stream belt of the target oil reservoir in the second embodiment of the present invention) and the grain size comparison of different sedimentary microfacies, it can be seen that the grain size of the main braided stream belt is coarser, with a high kurtosis, a small skewness, and good sorting, indicating relatively strong hydrodynamic force. The tail of its grain size probability distribution is thin, and the content of fine-grained substances such as mud is low, indicating that the change of the later sedimentary environment has little impact on the sediment, further indicating its stable hydrodynamic force. Therefore, overall, the hydrodynamic force of the main braided stream belt is strong and relatively stable, with obvious sedimentary differentiation. The sand body has characteristics such as coarse grain size, good sorting, relatively low mud content, and large sand body thickness.

[0081] For the secondary braided stream belt, the sedimentary environment changes rapidly. It can be seen from the core and mudstone color that the secondary braided stream belt is mostly in oxidized colors such as reddish brown, indicating a sedimentary environment exposed to the surface, showing that the water level is extremely low or even exposed to the surface in the dry season. Its hydrodynamic force is extremely unstable. Through the grain size probability distribution map (as Figure 5 shown, Figure 5 which is the grain size probability curve of the secondary braided stream belt of the target oil reservoir in the second embodiment of the present invention) and the grain size comparison of different sedimentary microfacies, it can be seen that the grain size of the secondary braided stream belt is relatively fine, with a low kurtosis, a large skewness, and poor sorting, indicating relatively weak hydrodynamic force and the characteristics of rapid deposition. The tail of its grain size probability distribution is thick, and even a small peak is formed, indicating that the later sedimentary environment changes greatly and the hydrodynamic force weakens rapidly. Overall, the sand body deposited in the secondary braided stream belt is mainly affected by episodic floods. During the flood period, the main braided stream belt breaches, and turbidity currents carry a large amount of sand and mud to deposit rapidly in the secondary braided stream belt. The sand and mud are mixed, with a large thickness and poor sorting. After the flood subsides, the hydrodynamic force decreases rapidly, and the content of suspended particles such as mud is high.

[0082] Based on the above content, it shows that the hydrodynamic force has an impact on the grain size in the low-resistivity oil reservoir. The larger the hydrodynamic force, the larger the grain size, and the smaller the hydrodynamic force, the corresponding smaller the grain size.

[0083] S220. Determine the influence of the microscopic characteristics of the low-resistivity oil reservoir on the resistivity.

[0084] Perform a relationship regression on the clay mineral content data and the median grain size data to clarify that the hydrodynamic force has an obvious impact on the clay minerals.

[0085] Regression analysis was performed on the data of the whole-rock content of montmorillonite and the resistivity data at the depth of the corresponding sampling points, and it was determined that clay minerals such as montmorillonite have an obvious control effect on the resistivity of low-resistivity oil reservoirs. Further, it was determined that resistivity is highly sensitive to the content of fine-grained components such as clay minerals in the reservoirs of low-resistivity oil reservoirs, and it has a stronger indicating effect on the microscopic characteristics and productivity of the reservoirs.

[0086] Clay minerals have a low specific gravity and are light minerals, and their distribution is greatly affected by hydrodynamic forces. The median grain size can indicate the strength of hydrodynamic forces. The larger the median grain size, the stronger the hydrodynamic forces. Regression analysis was performed on the whole-rock content of clay minerals and the median grain size, and it can be seen that there is an obvious negative correlation between the two, indicating that the stronger the hydrodynamic forces, the lower the clay mineral content (as Figure 6 shown, Figure 6 is the relationship diagram between the median grain size and the clay mineral content of the target oil reservoir in the second embodiment of the present invention). Compared with conventional oil reservoirs, the clay mineral content is higher during the deposition period of high irreducible water-clay additional conductivity type low-resistivity oil reservoirs, and the difference in hydrodynamic forces causes more significant differences in the clay mineral content in different facies belts.

[0087] Due to its strong cation exchange capacity, clay minerals have additional conductivity and can reduce the resistivity of reservoirs. Among them, montmorillonite has the strongest cation exchange capacity and the greatest influence on resistivity. Statistics were made on the whole-rock content of montmorillonite and the resistivity values at the corresponding sampling points, and regression analysis was performed on the two, and an obvious negative correlation can be seen ( Figure 7 is the relationship diagram between the resistivity and the whole-rock content of montmorillonite of the target oil reservoir in the second embodiment of the present invention), indicating that clay minerals have an obvious influence on the resistivity of this low-resistivity oil reservoir. The level of resistivity indicates the level of the content of fine-grained components such as clay minerals in the reservoir, and further indicates the microscopic characteristics of the reservoir physical properties. Since the clay mineral content varies greatly in different facies belts of this oil layer, it is thus proved that the resistivity of this low-resistivity oil reservoir is more sensitive to the microscopic characteristics of the reservoir and has a strong indicating significance for the productivity of the low-resistivity oil reservoir.

[0088] S230: Select wells where the low-resistivity oil reservoir has not been flooded during drilling and where single-layer PLT tests (fluid production profile tests) have been performed on the low-resistivity oil reservoir, collect fluid production profile data, MDT pressure measurement data or static pressure data, bottom-hole flowing pressure data, etc., and calculate the specific fluid production index of the low-resistivity oil reservoir.

[0089] Select wells where the low-resistivity oil reservoir has not been flooded during drilling and where single-layer PLT tests (fluid production profile tests) have been performed on the low-resistivity oil reservoir, collect PLT data (fluid production per day, L, m³ / day), and at the same time collect the corresponding thickness data (H, m) of the low-resistivity oil reservoir;

[0090] For the selected wells above, taking the two months before and after the PLT test as the window, collect the MDT pressure test data of the low-resistivity oil reservoirs in newly drilled wells nearby, and approximately regard it as the formation pressure (P1, MPa) during the PLT test. Additionally, if the vertical formation pressure in a well area is balanced and there is no reservoir pollution, collect the static formation pressure data of this well or nearby adjacent wells within two months before and after the PLT test, and convert it to the formation pressure (P1, MPa) of the low-resistivity oil reservoir.

[0091] For the selected wells above, collect the bottom-hole pressure monitoring data P J (MPa). Since the pressure monitoring device is located at the pump setting depth, therefore, according to the difference H J between the pump setting depth and the burial depth of the low-resistivity oil reservoir, combined with the oilfield pressure gradient, use the formula P2 = P J + H J / 100 * 1.05 (P2, MPa) to convert the bottom-hole flowing pressure.

[0092] According to the collected and converted data above, apply the formula Y = L / [(P1 - P2) * H] to convert the liquid production index Y of the low-resistivity oil reservoir, m³ / (day·m·MPa).

[0093] S240. Select wells in the early stage of oilfield development where the low-resistivity oil reservoir has not been flooded. According to the resistivity value distribution of the low-resistivity oil reservoir, divide the resistivity intervals and calculate the thickness ratio of each interval.

[0094] Divide the resistivity of the low-resistivity oil reservoir of all the selected wells into n intervals, respectively count the total thickness H of the low-resistivity oil reservoir of the selected wells, the thickness of each interval (h1…h n ), and calculate the thickness ratio (X1…X n );

[0095] Select wells in the early stage of oilfield development where the low-resistivity oil reservoir has not been flooded. Calculate and statistically analyze the average resistivity distribution probability of the low-resistivity oil reservoir. According to the resistivity distribution ( Figure 8 is the resistivity distribution probability histogram of the target oil reservoir in Embodiment 2 of the present invention), the resistivity of this low-resistivity oil reservoir is mainly distributed in 6 - 8 Ω·m and 8 - 10 Ω·m, and the proportion of both exceeds 25%. Therefore, divide the resistivity of the low-resistivity oil reservoir of all the selected wells into 4 intervals, namely 0 - 6 Ω·m, 6 - 8 Ω·m, 8 - 10 Ω·m, > 10 Ω·m, respectively count the total thickness H of the low-resistivity oil reservoir of the selected wells and the thicknesses h1, h2, h3, h4 of each resistivity interval, and calculate the thickness ratios X1, X2, X3, X4;

[0096] S250. Respectively perform relationship regression on the liquid production index Y of the low-resistivity oil reservoir calculated for each well and the thickness ratio (X1…X n ) of each resistivity interval to obtain n regression formulas Y n = f(Xn )

[0097] Analyze its positive and negative correlations and the correlation coefficient. Define the interval with a low correlation coefficient and a change in positive and negative correlations as the transition interval, take the median resistivity value in this interval, and determine it as the lower limit value of the resistivity of the effective contribution section of the low-resistivity oil reservoir. Using the above n regression formulas, calculate a set of fitting values of liquid production index Y1…Y n , as n sets of independent variables, take the liquid production index Y calculated in step S230 as the dependent variable, perform n-variable linear regression, and obtain the coefficients a1…a of each independent variable n and the intercept b. Finally, obtain the formula Y = a1Y1 + a2Y 2+ … + a n Y n + b = a1*f(X1) + a2*f(X2)… + a n *f(X n ).

[0098] Define the thickness ratios X1, X2, X3, X4 of the four resistivity intervals (0 - 6 Ω·m, 6 - 8 Ω·m, 8 - 10 Ω·m, ≥10 Ω·m) calculated for each well in step S240 as four independent variables. Perform a relationship regression between the liquid production index Y of the low-resistivity oil reservoir calculated for each well in step S230 and X1, X2, X3, X4 of the corresponding well respectively, obtain four regression formulas, and calculate a set of fitting liquid production indexes Y1, Y2, Y3, Y4 ( Figure 9 is the relationship diagram between the liquid production index and the thickness ratio of 0 - 6 Ω·m provided in Embodiment 2 of the present invention; Figure 10 is the relationship diagram between the liquid production index and the thickness ratio of 6 - 8 Ω·m provided in Embodiment 2 of the present invention; Figure 11 is the relationship diagram between the liquid production index and the thickness ratio of 8 - 10 Ω·m provided in Embodiment 2 of the present invention; Figure 12 is the relationship diagram between the liquid production index and the thickness ratio of >10 Ω·m provided in Embodiment 2 of the present invention).

[0099] Y1 = -50.60X1 3 + 68.79X1 2 - 30.75X1 + 5.51, R 2 = 0.54

[0100] Y2 = -30.43X2 3 + 46.92X2 2 - 22.58X2 + 4.35, R 2 = 0.29

[0101] Y3 = 1.68X3 + 1.26, R 2 = 0.01

[0102] Y4 = 34.75X4 3 - 32.82X4 2 + 11.06X4 + 0.32, R 2 = 0.80

[0103] For the resistivity range of 0 - 6 Ω·m, its thickness ratio shows an obvious negative correlation with the liquid production index per meter of pay zone, with a correlation coefficient of 0.54, indicating that the reservoir section in this range has a strong negative effect on the overall liquid production capacity of the reservoir. The larger the thickness ratio, the lower the liquid production index per meter of pay zone, and the poorer the liquid production capacity of the reservoir.

[0104] For the resistivity range of 6 - 8 Ω·m, its thickness ratio shows a certain negative correlation with the liquid production index per meter of pay zone, with a correlation coefficient of 0.29. However, compared with the range of 0 - 6 Ω·m, the negative correlation coefficient decreases, indicating that the negative effect of this reservoir section on the liquid production capacity has decreased. Overall, it also shows that the larger the ratio, the lower the liquid production index per meter of pay zone, and the poorer the liquid production capacity of the reservoir.

[0105] For the resistivity range of 8 - 10 Ω·m, the correlation between its thickness ratio and the liquid production index per meter of pay zone is the lowest, only 0.01, showing the characteristics of the transition from positive to negative correlation, indicating that starting from 8 - 10 Ω·m, this interval section begins to have a positive effect on the overall liquid production capacity of the reservoir. Take the median value of this range, 9 Ω·m, as the lower limit of the effective liquid production resistivity of this low-resistivity reservoir.

[0106] For the resistivity range of ≥10 Ω·m, its thickness ratio shows an obvious positive correlation with the liquid production index per meter of pay zone, and the correlation coefficient is relatively high, reaching 0.80. It shows that the reservoir in this interval section has a main controlling effect on the overall liquid production capacity and is the main contributing section.

[0107] Perform a regression analysis (Table 1) on the liquid production index per meter of pay zone Y calculated in step S230 and the above four groups of fitted liquid production indices per meter of pay zone Y1, Y2, Y3, Y4 to obtain the influence coefficients of the proportions of each interval on the production capacity;

[0108] Table 1

[0109] Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -0.21 1.33 -0.16 0.88 -2.96 2.54 -2.96 2.54 Y1 0.27 0.22 1.21 0.24 -0.19 0.73 -0.19 0.73 Y2 0.04 0.27 0.14 0.89 -0.51 0.59 -0.51 0.59 Y3 0.01 0.91 0.02 0.99 -1.86 1.89 -1.86 1.89 Y4 0.82 0.23 3.56 0.00 0.35 1.30 0.35 1.30

[0110] It can be seen that the proportion of the interval with resistivity ≥10 Ω·m has the largest influence coefficient on the production capacity (0.82), the proportion of the 0 - 6 Ω·m interval has the second largest influence (0.27), and the influence of the proportions of other intervals is relatively small (0.01 - 0.04). According to the influence coefficients, the fitting formula for the liquid production index per meter of pay zone is finally obtained:

[0111] Y = 0.27Y1 + 0.04Y2 + 0.01Y3 + 0.82Y4 = - 13.66X1 3 - 1.22X2 3 + 28.50X4 3 + 18.57X1 2+1.88X2 2 -26.91X4 2 -8.32X1 - 0.92X2 + 9.05X4 + 1.95, R 2 =0.81

[0112] The correlation coefficient is as high as 0.81, and it can accurately predict the liquid production index ratio of low-resistivity oil layers.

[0113] S260. Based on the thickness ratios (X1…X n ) of each interval of each well calculated in step S240, apply the formula Y = a1*f(X1) + a2*f(X2)… + a n *f(X n ) + b finally obtained in step S250 to calculate the liquid production index ratio Y of the low-resistivity oil reservoir of the wells selected in step S240, m³ / (day·m·MPa). Then, combined with the thickness H of the low-resistivity oil reservoir statistically obtained in step S240, apply the formula J = Y*H to calculate the liquid production index J of the low-resistivity oil reservoir, m³ / (day·MPa); Constrained by the sedimentary microfacies plan obtained in step S210, draw the isoline map of the liquid production index ratio of the entire oilfield and the isoline map of the liquid production index;

[0114] Exemplarily, using the thickness ratios X1, X2, X3, X4 calculated in the steps, apply the formula Y = 0.27Y1 + 0.04Y2 + 0.01Y3 + 0.82Y4 = -13.66X1 3 -1.22X2 3 +28.50X4 3 +18.57X1 2 +1.88X2 2 -26.91X4 2 -8.32X1 - 0.92X2 + 9.05X4 + 1.95 to calculate the liquid production index ratio of the low-resistivity oil reservoir of the wells selected in step S240. Based on the calculation results and the total thickness H of the low-resistivity oil reservoir of the selected wells statistically obtained in step S240, further calculate the liquid production index J = Y*H, m³ / (day·MPa).

[0115] According to the calculated liquid production index ratio and liquid production index data, under the control of the sedimentary microfacies map drawn in step S210, draw the isoline map of the liquid production index ratio of the entire oilfield ( Figure 13 is the isoline map of the liquid production index ratio of the low-resistivity oil reservoir in the target work area of the second embodiment of the present invention) and the isoline map of the liquid production index ( Figure 14 ).

[0116] S270. Select some wells as blind wells to verify the accuracy of the isochronal fluid production index contour map and fluid production index contour map drawn in step S260; based on the fluid production index contour map, combined with the actual situation of the remaining oil in the oilfield, set the lower limit of the fluid production index for horizontal well deployment to further delineate the favorable zones for horizontal well development; at the same time, delineate the areas with a fluid production index of 0 as unfavorable development zones to avoid ineffective perforations during production; finally, set the area between the favorable zones for horizontal well development and the unfavorable development zones as the favorable zones for directional well development, and the perforation positions should be combined with the lower resistivity limit value of the effective contribution section determined in S250, and effective perforations should be carried out in areas with resistivity higher than the lower limit value.

[0117] Select 3 horizontal wells in low-resistivity reservoirs as blind wells to verify the accuracy of the isochronal fluid production index contour map and fluid production index contour map drawn in step S260. At the pre-drilling stage of these 3 horizontal wells, the error between the predicted isochronal fluid production index using reservoir engineering methods or empirical methods and the actual value reached 0.7 - 3.9 m³ / (d·m·MPa), and the error rate was 36.8% - 264.3%; while using the isochronal fluid production index contour map to predict the isochronal fluid production index, the error compared with the actual value was only 0.1 - 0.4 m³ / (d·m·MPa), and the error rate was about 4.8% - 28.6%, with a significant improvement in accuracy, which proved the reliability of this method for predicting production capacity.

[0118] For oilfield development, especially for the deployment of horizontal wells, not only the liquid production capacity of the oil layer per unit thickness needs to be considered, but also the material basis needs to be considered to convert production capacity into production. Therefore, it is necessary to use the fluid production index to guide well location deployment and production prediction. Based on the fluid production index contour map drawn in step S260 and combined with the actual situation of oilfield development, set the lower limit of the fluid production index for horizontal well deployment to 25 m³ / (d·MPa). According to the common production pressure difference of 2.5 MPa for horizontal wells in the oilfield, it can ensure that the minimum daily liquid production of horizontal wells is about 60 m³ / d, with good development effects and economic efficiency. Using 25 m³ / (d·MPa) as the boundary, delineate the favorable zones for horizontal well development;

[0119] Based on the fluid production index contour map drawn in step S260, delineate the areas with a fluid production index of 0 as unfavorable development zones to avoid ineffective perforations during production, resulting in waste of resources;

[0120] Set the area between the above-mentioned delineated favorable zones for horizontal well development and the unfavorable development zones as the favorable zones for directional well development. During the development of directional wells, the perforation positions should refer to the lower resistivity limit value of 9 Ω·m of the effective contribution section determined in step S250, and effective perforations should be carried out at positions with resistivity higher than 9 Ω·m.

[0121] The technical solution of the embodiment of the present invention is to obtain the liquid production ratio data and resistance value distribution data of the low-resistivity reservoir in the reference well; determine the first regression function corresponding to the reference well based on the liquid production ratio data and the resistance value distribution data, and determine the predicted liquid production ratio data of the low-resistivity reservoir in the reference well based on the first regression function and the resistance value distribution data; determine the second regression function corresponding to the reference well based on the predicted liquid production ratio data, the liquid production ratio data, and the first regression function; and obtain the target predicted liquid production ratio data of the low-resistivity reservoir in the target well based on the second regression function and the target resistance value distribution data of the low-resistivity reservoir in the target well, so as to determine the productivity of the low-resistivity reservoir in the target well based on the target predicted liquid production ratio data. The technical solution of the embodiment of the present invention solves the problem that the existing technical methods mainly rely on high-end logging, with high costs and cannot be widely applied in the daily production of oil fields, and realizes reducing the dependence on high-end logging and achieving accurate prediction of the productivity of complex low-resistivity reservoirs based on conventional logging data.

[0122] Embodiment III

[0123] Figure 15 FIG. is a schematic structural diagram of a device for determining the productivity of a low-resistivity reservoir provided in Embodiment III of the present invention. As Figure 15 shown, the device includes:

[0124] A data acquisition module 310, configured to acquire the liquid production ratio data and the resistance value distribution data of the low-resistivity reservoir in the reference well; wherein, the resistance value distribution data includes the thickness ratio corresponding to the low-resistivity reservoir with different resistance values;

[0125] A liquid production ratio prediction module 320, configured to determine the first regression function corresponding to the reference well based on the liquid production ratio data and the resistance value distribution data, and determine the predicted liquid production ratio data of the low-resistivity reservoir in the reference well based on the first regression function and the resistance value distribution data;

[0126] A regression function determination module 330, configured to determine the second regression function corresponding to the reference well based on the predicted liquid production ratio data, the liquid production ratio data, and the first regression function;

[0127] A productivity determination module 340, configured to obtain the target predicted liquid production ratio data of the low-resistivity reservoir in the target well based on the second regression function and the target resistance value distribution data of the low-resistivity reservoir in the target well, so as to determine the productivity of the low-resistivity reservoir in the target well based on the target predicted liquid production ratio data.

[0128] The technical solution of the embodiment of the present invention is to obtain the liquid production ratio data and resistance value distribution data of the low-resistivity reservoir in the reference well; determine the first regression function corresponding to the reference well based on the liquid production ratio data and the resistance value distribution data, and determine the predicted liquid production ratio data of the low-resistivity reservoir in the reference well based on the first regression function and the resistance value distribution data; determine the second regression function corresponding to the reference well based on the predicted liquid production ratio data, the liquid production ratio data and the first regression function; obtain the target predicted liquid production ratio data of the low-resistivity reservoir in the target well based on the second regression function and the target resistance value distribution data of the low-resistivity reservoir in the target well, so as to determine the productivity of the low-resistivity reservoir in the target well based on the target predicted liquid production ratio data. The technical solution of the embodiment of the present invention solves the problem that the existing technical methods mainly rely on high-end logging, with high costs and unable to be widely applied in the daily production of oil fields, and realizes reducing the dependence on high-end logging, and can accurately predict the productivity of complex low-resistivity reservoirs based on conventional logging data.

[0129] Optionally, the productivity determination device for the low-resistivity reservoir further includes:

[0130] The to-be-used liquid production ratio data determination module is used to determine the corresponding existing resistance value distribution data of the low-resistivity reservoir in multiple existing wells, and obtain the to-be-used liquid production ratio data of each of the existing wells based on the existing resistance value distribution data and the second regression function of the resistance value;

[0131] The image drawing module is used to draw a liquid production ratio contour map and a liquid production index contour map corresponding to multiple existing wells based on each of the to-be-used liquid production ratio data on the basis of sedimentary facies control;

[0132] The target well selection module is used to select the well location of the target well before drilling based on the liquid production contour map and the liquid production index contour map.

[0133] Optionally, the data acquisition module 310 includes:

[0134] The reference well determination module is used to use the low-resistivity reservoir well in the preset area that has not been flooded and has undergone liquid production profile testing as the reference well, and obtain the daily output, thickness value and pressure value of the low-resistivity reservoir in the reference well;

[0135] The liquid production ratio calculation module is used to calculate the liquid production ratio data of the low-resistivity reservoir in the reference well based on the daily output, the thickness value and the pressure value.

[0136] Optionally, the data acquisition module 310 includes:

[0137] The interval division module is used to divide the resistance value range corresponding to the low-resistivity reservoir in the reference well into multiple resistance value intervals based on the resistance value range corresponding to the low-resistivity reservoir in the reference well and the interval division condition;

[0138] A resistance value distribution data determination module, configured to determine the thickness ratio of the low-resistivity oil reservoir corresponding to each resistance value interval in the reference well, and use the thickness ratio of the low-resistivity oil reservoir corresponding to each resistance value interval as the resistance value distribution data.

[0139] Optionally, the liquid production ratio prediction module 320 includes:

[0140] A first regression function determination module, configured to perform relationship regression calculation based on the liquid production ratio data and the resistance value distribution data to obtain a first regression function corresponding to the reference well.

[0141] Optionally, the liquid production ratio prediction module 320 includes:

[0142] A calculation module, configured to substitute the resistance value distribution data into the first regression function for calculation, obtain a calculation result corresponding to the resistance value distribution data, and use the calculation result as the liquid production ratio prediction data corresponding to the low-resistivity oil reservoir in the reference well.

[0143] Optionally, the regression function determination module 330 includes:

[0144] A second regression function to be processed determination module, configured to perform regression analysis based on the liquid production ratio data and the liquid production ratio prediction data of the low-resistivity oil reservoir in the reference well to obtain a second regression function to be processed;

[0145] A second regression function determination module, configured to obtain a second regression function corresponding to the reference well based on the first regression function and the second regression function to be processed.

[0146] Optionally, the production capacity determination module 340 is specifically configured to:

[0147] Substitute the target resistance value distribution data of the low-resistivity oil reservoir in the target well into the second regression function to obtain the target liquid production ratio prediction data of the low-resistivity oil reservoir in the target well.

[0148] The production capacity determination device for a low-resistivity oil reservoir provided by an embodiment of the present invention can execute the production capacity determination method for a low-resistivity oil reservoir provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0149] Embodiment 4

[0150] Figure 16FIG. 0 shows a schematic structural diagram of an electronic device 10 that can be used to implement an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as, for example, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0151] As Figure 16 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0152] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0153] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for determining the productivity of a low-resistivity reservoir.

[0154] In some embodiments, the method for determining the productivity of a low-resistivity reservoir can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by the processor 11, one or more steps of the method for determining the productivity of the low-resistivity reservoir described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the method for determining the productivity of the low-resistivity reservoir by any other suitable means (e.g., by means of firmware).

[0155] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), systems-on-chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0156] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when the computer programs are executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0157] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0158] To provide for interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide for interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0159] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0160] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The relationship between the client and the server is created by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0161] It should be understood that various forms of processes shown above can be used, with steps reordered, added or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0162] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for determining the productivity of a low-resistance reservoir, characterized in that, Including: Obtaining the liquid production ratio data and resistance value distribution data of low-resistivity oil reservoirs in a reference well; wherein, the resistance value distribution data includes the thickness ratio corresponding to low-resistivity oil reservoirs with different resistance values; Determining a first regression function corresponding to the reference well based on the liquid production ratio data and the resistance value distribution data, and determining the predicted liquid production ratio data of the low-resistivity oil reservoirs in the reference well based on the first regression function and the resistance value distribution data; Determining a second regression function corresponding to the reference well based on the predicted liquid production ratio data, the liquid production ratio data, and the first regression function, including: performing regression analysis on the liquid production ratio data and the predicted liquid production ratio data of the low-resistivity oil reservoirs in the reference well to obtain a second regression function to be processed; obtaining the second regression function corresponding to the reference well based on the first regression function and the second regression function to be processed; Obtaining the target predicted liquid production ratio data of the low-resistivity oil reservoirs in the target well based on the second regression function and the target resistance value distribution data of the low-resistivity oil reservoirs in the target well, so as to determine the productivity of the low-resistivity oil reservoirs in the target well based on the target predicted liquid production ratio data; Before obtaining the target predicted liquid production ratio data of the low-resistivity oil reservoirs in the target well based on the second regression function and the target resistance value distribution data of the low-resistivity oil reservoirs in the target well, it further includes: Determining the existing resistance value distribution data corresponding to the low-resistivity oil reservoirs in multiple existing wells, and obtaining the to-be-used liquid production ratio data of each of the existing wells based on the existing resistance value distribution data and the second regression function of resistance values; On the basis of sedimentary facies control, drawing a liquid production ratio isogram and a liquid production index isogram corresponding to multiple existing wells based on the to-be-used liquid production ratio data of each of them; Selecting the well location of the target well before drilling based on the liquid production isogram and the liquid production index isogram; 2. The method according to claim 1, wherein The obtaining of the liquid production ratio data of the low-resistivity oil reservoirs in the reference well includes: Taking the low-resistivity oil reservoir well in the preset area that has not been flooded and has undergone liquid production profile testing as the reference well, and obtaining the daily production, thickness value, and pressure value of the low-resistivity oil reservoir in the reference well; Calculating the liquid production ratio data of the low-resistivity oil reservoirs in the reference well based on the daily production, the thickness value, and the pressure value; 3. The method according to claim 1, characterized in that The obtaining of the resistance value distribution data of the low-resistivity oil reservoirs in the reference well includes: Dividing the resistance value range corresponding to the low-resistivity oil reservoir in the reference well into multiple resistance value intervals based on the resistance value range and interval division conditions corresponding to the low-resistivity oil reservoir in the reference well; Determining the thickness ratio of the low-resistivity oil reservoir corresponding to each resistance value interval in the reference well, and taking the thickness ratio of the low-resistivity oil reservoir corresponding to each resistance value interval as the resistance value distribution data; 4. The method according to claim 1, wherein The determining of the first regression function corresponding to the reference well based on the liquid production ratio data and the resistance value distribution data includes: Performing relationship regression calculation based on the liquid production ratio data and the resistance value distribution data to obtain a first regression function corresponding to the reference well; 5. The method according to claim 1, wherein The determining of the predicted liquid production ratio data of the low-resistivity oil reservoirs in the reference well based on the first regression function and the resistance value distribution data includes: Substitute the resistance value distribution data into the first regression function for calculation to obtain a calculation result corresponding to the resistance value distribution data, and use the calculation result as the predicted liquid production ratio data corresponding to the low-resistivity oil reservoir in the reference well.

6. The method according to claim 1, wherein The obtaining of the target predicted liquid production ratio data of the low-resistivity oil reservoir in the target well based on the second regression function and the target resistance value distribution data of the low-resistivity oil reservoir in the target well includes: Substitute the target resistance value distribution data of the low-resistivity oil reservoir in the target well into the second regression function to obtain the target predicted liquid production ratio data of the low-resistivity oil reservoir in the target well.

7. A device for determining the productivity of a low-resistance oil reservoir, characterized in that, Includes: A data acquisition module, configured to acquire the liquid production ratio data and the resistance value distribution data of the low-resistivity oil reservoir in the reference well; wherein, the resistance value distribution data includes the thickness ratio corresponding to the low-resistivity oil reservoir with different resistance values. A liquid production ratio prediction module, configured to determine the first regression function corresponding to the reference well based on the liquid production ratio data and the resistance value distribution data, and determine the predicted liquid production ratio data of the low-resistivity oil reservoir in the reference well based on the first regression function and the resistance value distribution data. A regression function determination module, configured to determine the second regression function corresponding to the reference well based on the predicted liquid production ratio data, the liquid production ratio data, and the first regression function. A production capacity determination module, configured to obtain the target predicted liquid production ratio data of the low-resistivity oil reservoir in the target well based on the second regression function and the target resistance value distribution data of the low-resistivity oil reservoir in the target well, so as to determine the production capacity of the low-resistivity oil reservoir in the target well based on the target predicted liquid production ratio data. The production capacity determination device for the low-resistivity oil reservoir further includes: A to-be-used liquid production ratio data determination module, configured to determine the corresponding existing resistance value distribution data of the low-resistivity oil reservoir in multiple existing wells, and obtain the to-be-used liquid production ratio data of each existing well based on the existing resistance value distribution data and the second regression function of the resistance value. An image drawing module, configured to draw a liquid production ratio isogram and a liquid production index isogram corresponding to multiple existing wells based on each to-be-used liquid production ratio data on the basis of sedimentary facies control. A target well selection module, configured to select the well location of the target well before drilling based on the liquid production isogram and the liquid production index isogram. The regression function determination module includes: A to-be-processed second regression function determination module, configured to perform regression analysis based on the liquid production ratio data and the predicted liquid production ratio data of the low-resistivity oil reservoir in the reference well to obtain a to-be-processed second regression function. A second regression function determination module, configured to obtain the second regression function corresponding to the reference well based on the first regression function and the to-be-processed second regression function.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the production capacity determination method for the low-resistivity oil reservoir according to any one of claims 1-6.

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