CN112541607B - Volcanic oil reservoir productivity prediction method and device - Google Patents
Volcanic oil reservoir productivity prediction method and device Download PDFInfo
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Abstract
The application provides a volcanic oil reservoir productivity prediction method and device, wherein the method comprises the following steps: acquiring volcanic reservoir description indexes of a first target oil well with a confirmed effective reservoir; determining the type of the effective reservoir according to the volcanic reservoir description index so as to determine the reservoir thickness of the effective reservoir according to the type of the effective reservoir; obtaining two different thickness waiting coefficients corresponding to the reservoir thickness; and predicting the accumulated oil yield of the first target oil well in the accumulated oil yield prediction time according to a preset accumulated oil yield prediction time and two different thickness undetermined coefficients, so as to determine the development scheme of the first target oil well based on a corresponding prediction result. The method can improve the accuracy of the yield prediction of the volcanic oil reservoir, and further improve the high efficiency of the high-quality reservoir development of the volcanic oil reservoir.
Description
Technical Field
The application relates to the technical field of development of special lithology oil reservoirs, in particular to a volcanic oil reservoir productivity prediction method and device.
Background
In the world petroleum exploration and development process, clastic rock and carbonate rock reservoirs are often main objects of petroleum reservoir exploration and development, while volcanic rock reservoirs which are relatively deep are buried, and the reservoir identification and distribution prediction have great difficulty due to the complex lithology and lithology characteristics, so that the clastic rock and the carbonate rock reservoirs are often ignored by us. Since the beginning of the 20 th century, the development of petroleum industry and the development of exploration and development technology have been improved continuously, and the exploration and development of volcanic hydrocarbon reservoirs at home and abroad have been successful, such as the fields of the United states, the Goba, japan, canada and the like, the domestic great harbors, liaohe, xinjiang, daqing and the like, and the volcanic hydrocarbon reservoirs have gradually become important fields for oil and gas exploration and development, and the reservoir characteristics thereof have become the key points for the research of special lithology reservoirs. At present, scholars at home and abroad have made great progress in the aspects of volcanic lithology and lithology phase identification, reservoir space, volcanic reservoir logging interpretation model and the like, and have obtained extensive consensus in the aspects of volcanic lithology, physical properties, reservoir space and the like.
Aiming at productivity prediction, the research on the aspect of gas reservoirs and based on seepage characteristics is mainly focused at present, an unsteady productivity prediction model (Wang Jiang and the like, 2014) is established by considering seepage characteristics and mechanisms, and a novel productivity prediction method (Wang Zhiping, 2014) for mutually interfering a plurality of transverse cracks of volcanic gas reservoir horizontal well fracturing is established on the basis of an equivalent seepage flow method and a superposition principle. Conventional clastic reservoirs utilize logging data to predict productivity, and one type is to establish a statistical relationship between the reservoir capacity and the productivity directly based on a large amount of logging, logging interpretation and core analysis data, for example Ouyang Jian et al (1994) to evaluate the reservoir capacity by using the effective permeability and the oil saturation of the reservoir; the other is to use mathematical algorithms, such as principal component analysis, fuzzy mathematics and the like, to predict the productivity of the reservoir (Li Rui, 2003, tan Chengqian, 2004, luo Yu, 2003) so as to achieve a certain effect, but not completely suitable for quantitative productivity prediction of volcanic seams and Kong Shuangchong medium reservoirs.
Disclosure of Invention
Aiming at the problems in the prior art, the application provides a volcanic oil reservoir productivity prediction method and device, which can improve the accuracy of the volcanic oil reservoir productivity prediction, and further improve the high efficiency of volcanic oil reservoir high-quality reservoir development.
In order to solve the technical problems, the application provides the following technical scheme:
in a first aspect, the application provides a method for predicting the capacity of a volcanic oil reservoir, comprising the following steps:
acquiring volcanic reservoir description indexes of a first target oil well with a confirmed effective reservoir;
Determining the type of the effective reservoir according to the volcanic reservoir description index so as to determine the reservoir thickness of the effective reservoir according to the type of the effective reservoir;
Obtaining two different thickness waiting coefficients corresponding to the reservoir thickness;
and predicting the accumulated oil yield of the first target oil well in the accumulated oil yield prediction time according to a preset accumulated oil yield prediction time and two different thickness undetermined coefficients, so as to determine the development scheme of the first target oil well based on a corresponding prediction result.
Further, before the obtaining the volcanic rock reservoir description index of the first target oil well for which the existence of the effective reservoir is confirmed, the method further comprises: acquiring the reservoir thickness of each effective reservoir of a second target oil well, a plurality of accumulated production times and accumulated oil production in the accumulated production time, wherein the second target oil well and the first target oil well are in the same preset area; acquiring reservoir thicknesses of effective reservoirs of a plurality of oil wells in the preset area; fitting a plurality of accumulated production times of the second target oil well and accumulated oil production in the accumulated production time to obtain a first historical thickness pending coefficient and a second historical thickness pending coefficient; fitting the first historical thickness undetermined coefficient and reservoir thicknesses of the effective reservoirs of the plurality of oil wells to obtain a first fitting undetermined coefficient group; fitting the second historical thickness pending coefficient with reservoir thicknesses of each of the plurality of active reservoirs of the well to obtain a second set of fitted pending coefficients.
Further, the volcanic reservoir description index includes: an oil reservoir quality factor and a fracture density, wherein the oil reservoir quality factor comprises: reservoir permeability and porosity; correspondingly, the determining the type of the effective reservoir according to the volcanic reservoir description index comprises the following steps: acquiring corresponding classification discrimination parameters according to the oil reservoir quality factors and the fracture density; normalizing the classification discrimination parameters, and determining the type of the effective reservoir according to the normalized classification discrimination parameters, wherein the type of the effective reservoir comprises: one type of effective reservoir, two types of effective reservoirs and three types of effective reservoirs.
Further, the obtaining two different thickness pending coefficients corresponding to the reservoir thickness includes: and obtaining a first thickness undetermined coefficient through the reservoir thickness and the first fitting undetermined coefficient group, and obtaining a second thickness undetermined coefficient through each reservoir thickness and the second fitting undetermined coefficient group.
In a second aspect, the present application provides a volcanic oil reservoir capacity prediction apparatus, including:
the first acquisition module is used for acquiring volcanic reservoir descriptive indexes of a first target oil well with the confirmed effective reservoir;
The judging module is used for determining the type of the effective reservoir according to the volcanic reservoir description index so as to determine the reservoir thickness of the effective reservoir according to the type of the effective reservoir;
The second acquisition module is used for acquiring two different thickness waiting coefficients corresponding to the reservoir thickness;
The prediction module is used for predicting the accumulated oil yield of the first target oil well in the accumulated oil yield prediction time according to the preset accumulated oil yield prediction time and the two different thickness undetermined coefficients so as to determine the development scheme of the first target oil well based on the corresponding prediction result.
Further, the volcanic oil reservoir productivity prediction device further comprises:
The historical data acquisition module is used for acquiring the reservoir thickness of each effective reservoir of the second target oil well, a plurality of accumulated production time and accumulated oil production in the accumulated production time, wherein the second target oil well and the first target oil well are in the same preset area; the third acquisition module is used for acquiring the reservoir thicknesses of the effective reservoirs of the plurality of oil wells in the preset area; the first fitting module is used for fitting a plurality of accumulated production times of the second target oil well and accumulated oil production in the accumulated production time to obtain a first historical thickness undetermined coefficient and a second historical thickness undetermined coefficient; the second fitting module is used for fitting the first historical thickness undetermined coefficient and reservoir thicknesses of the effective reservoirs of the plurality of oil wells to obtain a first fitting undetermined coefficient group; and the third fitting module is used for fitting the second historical thickness undetermined coefficient and reservoir thicknesses of the effective reservoirs of the plurality of oil wells to obtain a second fitting undetermined coefficient group.
Further, the volcanic reservoir description index includes: an oil reservoir quality factor and a fracture density, wherein the oil reservoir quality factor comprises: reservoir permeability and porosity; correspondingly, the judging module comprises: the classification discrimination parameter obtaining unit is used for obtaining corresponding classification discrimination parameters according to the oil reservoir quality factors and the fracture density; the normalization unit is used for carrying out normalization processing on the classification discrimination parameters and determining the type of the effective reservoir according to the classification discrimination parameters after normalization processing, wherein the type of the effective reservoir comprises: one type of effective reservoir, two types of effective reservoirs and three types of effective reservoirs.
Further, the second acquisition module further includes: and the undetermined coefficient acquisition unit is used for acquiring the first thickness undetermined coefficient through the reservoir thickness and the first fitting undetermined coefficient group and acquiring the second thickness undetermined coefficient through each reservoir thickness and the second fitting undetermined coefficient group.
In a third aspect, the present application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the volcanic reservoir capacity prediction method when executing the program.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon computer instructions which when executed perform the steps of the volcanic reservoir capacity prediction method.
As can be seen from the above technical solutions, the embodiments of the present application provide a method and an apparatus for predicting the productivity of a volcanic oil reservoir, where the method for predicting the productivity of the volcanic oil reservoir includes: acquiring volcanic reservoir description indexes of a first target oil well with a confirmed effective reservoir; determining the type of the effective reservoir according to the volcanic reservoir description index so as to determine the reservoir thickness of the effective reservoir according to the type of the effective reservoir; obtaining two different thickness waiting coefficients corresponding to the reservoir thickness; and predicting the accumulated oil yield of the first target oil well in the accumulated oil yield prediction time according to a preset accumulated oil yield prediction time and two different thickness undetermined coefficients, so as to determine the development scheme of the first target oil well based on a corresponding prediction result. The method has the advantages that the volcanic reservoir can be layered more accurately, the degree of coincidence between the layering result and the on-site actual effect is high, the yield of the volcanic reservoir can be predicted quantitatively, the accuracy and the high efficiency of prediction are improved, the high efficiency of volcanic reservoir development is further improved, the basis is provided for development and deployment, the development cost is saved, and the execution process is simple and reliable.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for predicting the capacity of a volcanic reservoir according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of obtaining a first thickness undetermined coefficient and a second thickness undetermined coefficient in a volcanic oil reservoir productivity prediction method according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of steps 201 to 202 in a volcanic reservoir capacity prediction method according to an embodiment of the present application;
FIG. 4 is a schematic structural diagram of a volcanic reservoir capacity prediction apparatus according to an embodiment of the present application;
FIG. 5 is a schematic structural diagram of each module for obtaining a first thickness pending coefficient and a second thickness pending coefficient in a volcanic reservoir capacity prediction apparatus according to an embodiment of the present application;
FIG. 6 is a schematic structural diagram of a judging module of a volcanic oil reservoir productivity predicting device according to an embodiment of the present application;
FIG. 7 is a schematic diagram of a result of a second acquisition module of a volcanic reservoir capacity prediction apparatus according to an embodiment of the present application;
FIG. 8 is a schematic diagram of the relationship between cumulative oil production and cumulative production time for a single well of a pseudo-alloy dragon 2 well zone Jia mu river volcanic reservoir in an example of an application of the present application;
FIG. 9 is a graph showing the cumulative yield prediction results of the one year period of the Jinlong 2 well zone jia mu river volcanic reservoir in an embodiment of the present application;
fig. 10 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions in the present specification better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Based on the method, in order to improve the accuracy of the yield prediction of the volcanic oil reservoir, the high efficiency of the high-quality reservoir development of the volcanic oil reservoir is further improved. Considering the change of the existing productivity prediction method, the volcanic oil reservoir productivity prediction algorithm is established by taking geological features as the basis and combining the production dynamic data fitting relation. The method can rapidly predict the energy production of the effective reservoir of the volcanic oil reservoir and provide technical guidance and reference for the efficient development of the volcanic oil reservoir. Specifically, a corresponding fitting curve for predicting oil production predictions of an oil well in a target area and each oil well in the target area is obtained according to historical accumulated oil production and accumulated production time of the oil well in the target area. The more the historical data is, the higher the prediction accuracy is, and the method can be suitable for quantitative prediction of the volcanic oil reservoir productivity, and has the advantages of simple prediction process and development cost saving.
According to the foregoing, the embodiment of the application provides a volcanic oil reservoir capacity prediction apparatus, which may be a server or a client device, where the client device may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), a vehicle-mounted device, an intelligent wearable device, and the like. Wherein, intelligent wearing equipment can include intelligent glasses, intelligent wrist-watch and intelligent bracelet etc..
In practical applications, the part for performing volcanic reservoir capacity prediction may be performed on the server side as described above, or all operations may be performed in the client device. Specifically, the selection may be made according to the processing capability of the client device, and restrictions of the use scenario of the user. The application is not limited in this regard. If all operations are performed in the client device, the client device may further include a processor.
The client device may have a communication module (i.e. a communication unit) and may be connected to a remote server in a communication manner, so as to implement data transmission with the server. The server may include a server on the side of the task scheduling center, and in other implementations may include a server of an intermediate platform, such as a server of a third party server platform having a communication link with the task scheduling center server. The server may include a single computer device, a server cluster formed by a plurality of servers, or a server structure of a distributed device.
Any suitable network protocol may be used for communication between the server and the client device, including those not yet developed on the filing date of the present application. The network protocols may include, for example, TCP/IP protocol, UDP/IP protocol, HTTP protocol, HTTPS protocol, etc. Of course, the network protocol may also include, for example, RPC protocol (Remote Procedure Call Protocol ), REST protocol (Representational STATE TRANSFER) or the like used above the above-described protocol.
The following examples are presented in detail.
In order to improve the accuracy of the yield prediction of the volcanic oil reservoir and further improve the high efficiency of the development of the high-quality reservoir of the volcanic oil reservoir, the application provides a volcanic oil reservoir yield prediction method, the execution subject of which is a volcanic oil reservoir yield prediction device, and referring to fig. 1, the method specifically comprises the following steps:
Step 100: a volcanic reservoir description index for a first target well for which a valid reservoir has been identified is obtained.
Specifically, the first target oil well is an oil well for which oil production is to be predicted, and the volcanic reservoir description index includes an oil reservoir quality factor and a fracture density, wherein the oil reservoir quality factor includes: reservoir permeability and porosity; the lithology of different volcanic reservoirs is different in electrical parameters and crack development density, and the oil layer quality of the volcanic pore and crack dual-medium oil reservoir is evaluated by classification through the oil reservoir quality factors and the crack development density.
Step 200: and determining the type of the effective reservoir according to the volcanic reservoir description index so as to determine the reservoir thickness of the effective reservoir according to the type of the effective reservoir.
Specifically, an oil reservoir quality factor RQI and a crack development density n are applied to establish a classification discriminant function:
Z＝RQI+n×RQI
Wherein Z is a classification discrimination parameter, K is permeability and phi is porosity. Transmitting the volcanic reservoir description index into the classification discrimination function to obtain a corresponding classification discrimination parameter Z; after normalization processing is carried out on the classification discrimination parameters, determining the type of the effective reservoir corresponding to the first target oil well according to the classification discrimination parameters and a preset effective reservoir interval. The types of the effective reservoirs comprise one type of effective reservoir type, two types of effective reservoir type and three types of effective reservoir type, for example, Z is normalized by classification discrimination parameters, and if Z is more than or equal to 0.7, one type of effective reservoir exists in the first target oil well; z is more than 0.7 and is more than or equal to 0.3, and the first target oil well has two kinds of effective reservoirs; if Z is less than 0.3, three effective reservoirs exist in the first target oil well; the first target oil well has at least one type of effective reservoir, and the respective thickness of each type of effective reservoir of the first target oil well can be determined according to the type of the effective reservoir. Specifically, the preset effective reservoir interval may be set according to practical situations, which is not limited by the present application.
Step 300: and obtaining two different thickness undetermined coefficients corresponding to the reservoir thickness.
Specifically, the relation between the accumulated oil yield and the accumulated oil yield prediction time is as follows, wherein a and b are the two different thickness undetermined coefficients, t is the accumulated oil yield prediction time, and Q is the accumulated oil yield in the accumulated oil yield prediction time of the first target oil well.
Q＝a×(1+t)b
Step 400: and predicting the accumulated oil yield of the first target oil well in the accumulated oil yield prediction time according to a preset accumulated oil yield prediction time and two different thickness undetermined coefficients, so as to determine the development scheme of the first target oil well based on a corresponding prediction result.
Specifically, the relation between the accumulated oil yield and the accumulated oil yield prediction time is applied to predict the accumulated oil yield in the preset accumulated oil yield prediction time. The preset accumulated oil production prediction time can be set according to actual needs, and the application is not limited to this.
Referring to fig. 2, in order to further improve accuracy of the volcanic rock mineral production capacity prediction, in one or more embodiments of the present application, before step 100, the method further includes:
Step 001: and acquiring the reservoir thickness of each effective reservoir of the second target oil well, a plurality of accumulated production time and accumulated oil production in the accumulated production time, wherein the second target oil well and the first target oil well are in the same preset area.
Specifically, the preset area may be set according to actual situations, which is not limited by the present application. The reservoir thickness of each effective reservoir, a plurality of accumulated production time and accumulated oil production in the accumulated production time are historical data acquired before the current system time, and a plurality of accumulated production times acquired before the current system time and accumulated oil production corresponding to each accumulated time are acquired.
Step 002: and acquiring the reservoir thicknesses of the effective reservoirs of the plurality of oil wells in the preset area.
Specifically, all effective reservoirs contained in at least three oil wells in the preset area are obtained, and the respective thicknesses of all the effective reservoirs are obtained.
Step 003: fitting a plurality of accumulated production times of the second target oil well and accumulated oil production in the accumulated production time to obtain a first historical thickness pending coefficient and a second historical thickness pending coefficient.
Specifically, a plurality of accumulated production time t 'and accumulated oil yield Q' in the accumulated production time are input into the following expression, an image of the expression is fitted, and a corresponding first historical thickness coefficient A and a corresponding second historical thickness coefficient B are obtained according to a fitting result. It is understood that any one of the accumulated production time and the accumulated oil production during the accumulated production time can be brought into the following expression as a set of values.
Q'＝A×(1+t')B
Step 004: fitting the first historical thickness pending coefficient with reservoir thicknesses of each of the plurality of active reservoirs of the well to obtain a first set of fitted pending coefficients.
Specifically, the first historical thickness undetermined coefficient and the reservoir thickness of each effective reservoir of any oil well are input into the following expression to obtain a group of corresponding relations, wherein the corresponding relations are the corresponding relations between the first historical thickness undetermined coefficient and the reservoir thickness of each effective reservoir of any oil well, the reservoir thickness of each effective reservoir of another oil well is input according to the same mode, a plurality of groups of corresponding relations are obtained, and the first fitting undetermined coefficient group is obtained according to the plurality of groups of corresponding relations. In this expression, h 'I、h'II and h' III represent the reservoir thicknesses of one type of effective reservoir, two types of effective reservoirs, and three types of effective reservoirs, respectively, in the same well. x1, x2 and x3 represent a first set of fitted coefficients to be determined, a being a first historical thickness coefficient to be determined.
A＝x1h'I+x2h'II+x3h'III
Step 005: fitting the second historical thickness pending coefficient with reservoir thicknesses of each of the plurality of active reservoirs of the well to obtain a second set of fitted pending coefficients.
Specifically, the second historical thickness undetermined coefficient and the reservoir thickness of each effective reservoir of any oil well are input into the following expression for a plurality of times to obtain a group of corresponding relations, wherein the corresponding relations are the corresponding relations between the second historical thickness undetermined coefficient and the reservoir thickness of each effective reservoir of any oil well, the reservoir thickness of each effective reservoir of another oil well is input according to the same mode, and a plurality of groups of corresponding relations are obtained to obtain the second fitting undetermined coefficient group. In this expression, h 'I、h'II and h' III represent the reservoir thicknesses of one type of effective reservoir, two types of effective reservoirs, and three types of effective reservoirs, respectively, in the same well. y1, y2 and y3 represent a second set of fitted coefficients to be determined, and B is a second historical thickness coefficient to be determined.
B＝y1h'I+y2h'II+y3h'III
And taking the finally obtained first fitting undetermined coefficient group and the second fitting undetermined coefficient group as necessary data for predicting the capacity of the volcanic oil reservoir in real time, and determining two thickness undetermined coefficients of the oil well to be measured.
Referring to fig. 3, in order to improve the accuracy of determining whether an active reservoir is present and the reservoir thickness of each active reservoir in an oil well, in one or more embodiments of the present application, step 200 includes:
step 201: and obtaining corresponding classification discrimination parameters according to the oil reservoir quality factors and the fracture density.
Step 202: normalizing the classification discrimination parameters, and determining the type of the effective reservoir according to the normalized classification discrimination parameters, wherein the type of the effective reservoir comprises: one type of effective reservoir, two types of effective reservoirs and three types of effective reservoirs.
Specifically, in combination with dynamic production data of an oil testing and production well, an effective reservoir mainly comprises an Anshan sintered breccia/breccia lava, the crack density is more than 1.5, the reservoir quality and seepage capability are excellent, and the oil testing and production index is higher; the second-class effective reservoir is mainly composed of almond basalt, andesite and volcanic breccia, the crack density is between 1 and 1.5, the physical properties of the reservoir are inferior, the RQI value is relatively smaller, and the RQI value is not more different from the first-class oil extraction index; the three effective reservoirs mainly comprise tuff and flow rock, the physical properties of the reservoirs are poor, the crack density is less than 1, but the three effective reservoirs still have certain seepage and output capacity after fracturing, and the potential is between effective and effective reservoirs.
In order to further improve the accuracy of predicting the capacity of the volcanic reservoir, in one or more embodiments of the present application, step 300 includes:
Step 301: and obtaining a first thickness undetermined coefficient through the reservoir thickness and the first fitting undetermined coefficient group, and obtaining a second thickness undetermined coefficient through each reservoir thickness and the second fitting undetermined coefficient group.
Specifically, inputting the reservoir thickness and the first fitting coefficient to the following expression to obtain a first thickness coefficient, wherein h I、hII and h III respectively represent the reservoir thicknesses of one type of effective reservoir, two types of effective reservoirs and three types of effective reservoirs in an oil well to be tested; x1, x2 and x3 represent a first fitting pending coefficient group; a is a first thickness pending coefficient.
a＝x1hI+x2hII+x3hIII
Specifically, inputting the reservoir thickness and the second fitting coefficient to the following expression to obtain a second thickness coefficient, wherein h I、hII and h III respectively represent the reservoir thicknesses of one type of effective reservoir, two types of effective reservoirs and three types of effective reservoirs in the oil well to be tested; x1, x2 and x3 represent a first fitting pending coefficient group; b is a second thickness pending coefficient.
b＝y1hI+y2hII+y3hIII
In order to improve accuracy of the prediction of the yield of the volcanic oil reservoir and further improve efficiency of the development of the high-quality reservoir of the volcanic oil reservoir, the application provides an embodiment of a volcanic oil reservoir yield prediction device for executing all or part of the content in the volcanic oil reservoir yield prediction method, and the volcanic oil reservoir yield prediction device, referring to fig. 4, specifically includes the following contents:
A first acquisition module 10 for acquiring volcanic reservoir description indicators of a first target well for which a valid reservoir has been identified.
The volcanic reservoir description index comprises: an oil reservoir quality factor and a fracture density, wherein the oil reservoir quality factor comprises: reservoir permeability and porosity.
A judging module 20, configured to determine the type of the effective reservoir according to the volcanic reservoir description index, so as to determine the reservoir thickness of the effective reservoir according to the type of the effective reservoir.
And the second acquisition module 30 is used for acquiring two different thickness undetermined coefficients corresponding to the reservoir thickness.
The prediction module 40 is configured to predict, according to a preset cumulative oil production prediction time and two different thickness pending coefficients, a cumulative oil production of the first target oil well within the cumulative oil production prediction time, so as to determine a development scheme of the first target oil well based on a corresponding prediction result.
Referring to fig. 5, in order to further improve accuracy and efficiency of the volcanic oil reservoir productivity prediction, in one or more embodiments of the present application, the volcanic oil reservoir productivity prediction apparatus further includes:
The historical data acquisition module 50 is configured to acquire a reservoir thickness of each effective reservoir of the second target oil well, a plurality of accumulated production times, and accumulated oil production during the accumulated production times, where the second target oil well and the first target oil well are in a same preset area.
And a third acquisition module 60, configured to acquire reservoir thicknesses of effective reservoirs of the plurality of oil wells in the preset area.
The first fitting module 70 is configured to fit a plurality of accumulated production times of the second target oil well and accumulated oil production during the accumulated production times to obtain a first historical thickness pending coefficient and a second historical thickness pending coefficient.
A second fitting module 80 is configured to fit the first historical thickness pending coefficients to reservoir thicknesses of each of the plurality of active reservoirs of the well to obtain a first set of fitted pending coefficients.
A third fitting module 90 is configured to fit the second historical thickness pending coefficients to reservoir thicknesses of each of the plurality of active reservoirs of the well to obtain a second set of fitted pending coefficients.
Referring to fig. 6, in order to further improve accuracy and efficiency of the volcanic reservoir productivity prediction, in one or more embodiments of the present application, the determining module 20 includes:
and the classification discrimination parameter acquisition unit 21 is used for acquiring corresponding classification discrimination parameters according to the oil reservoir quality factors and the fracture density.
A normalizing unit 22, configured to normalize the classification discrimination parameter, and determine a type of the effective reservoir according to the classification discrimination parameter after normalization, where the type of the effective reservoir includes: one type of effective reservoir, two types of effective reservoirs and three types of effective reservoirs.
Referring to fig. 7, in order to further improve accuracy and efficiency of the volcanic reservoir productivity prediction, in one or more embodiments of the present application, the second acquisition module 30 further includes:
a coefficient of uncertainty obtaining unit 31, configured to obtain a coefficient of uncertainty of a first thickness via the reservoir thickness and the first fitting coefficient set, and obtain a coefficient of uncertainty of a second thickness via each of the reservoir thickness and the second fitting coefficient set.
In order to further improve the accuracy of quantitative classification evaluation of reservoirs and the accuracy of oil reservoir yield prediction, the application provides a specific application example of a volcanic oil reservoir yield prediction method. The method specifically comprises the following steps:
1. quantitative classification evaluation of reservoirs
Firstly, carrying out classification evaluation on the oil layer according to the fine description result of the volcanic reservoir. The application considers the electrical parameter difference between different lithology of volcanic reservoirs and the crack development density of different lithology reservoirs, selects the oil reservoir quality factor RQI and the crack development density n to jointly classify and evaluate the oil reservoir quality of the volcanic pore and crack dual-medium oil reservoir, and establishes a classification discriminant function Z, whereinK is permeability, phi is porosity, n is fracture density.
Z＝RQI+n×RQI
Normalizing the parameter Z, wherein Z is more than or equal to 0.7, and is an effective reservoir; z is more than 0.7 and is more than or equal to 0.3, and is a second-class effective reservoir; z is less than 0.3, and is three effective reservoirs. In combination with dynamic production data of the oil testing and production well, an effective reservoir mainly comprises an Anshan sintered breccia/breccia lava, the crack density is greater than 1.5, the reservoir quality and seepage capability are excellent, and the oil testing and production index is higher; the second-class effective reservoir is mainly composed of almond basalt, andesite and volcanic breccia, the crack density is between 1 and 1.5, the physical properties of the reservoir are inferior, the RQI value is relatively smaller, and the RQI value is not more different from the first-class oil extraction index; the three effective reservoirs mainly comprise tuff and flow rock, the physical properties of the reservoirs are poor, the crack density is less than 1, but the three effective reservoirs still have certain seepage and output capacity after fracturing, and the potential is between effective and effective reservoirs.
2. Reservoir capacity prediction
According to the geological characteristics of the volcanic reservoir, the method for predicting the effective reservoir productivity is provided by combining production dynamic data, and a reference basis is provided for development and deployment. The method comprises the following 2 steps:
1) Fitting a relation between the accumulated oil yield and the accumulated production time of the single well according to actual production of the production test well:
Q＝a×(1+t)b (1)
Wherein Q is the cumulative oil production, ton; t-cumulative production time, day; a. b-a pending factor related to the effective reservoir thickness; the coefficients of the fitting relationship between cumulative production and production time for different wells are different.
2) Carrying out productivity prediction on the oil well; and combining the reservoir classification, counting the thickness of various oil layers of different wells, and establishing a function:
a＝x1hI+x2hII+x3hIII (2)
b＝y1hI+y2hII+y3hIII (3)
In the formula, h n is the thickness of an oil layer and meter; x, y-fitting the undetermined coefficients; and (3) bringing the a and b into a formula (1) to obtain the predicted value of the single well oil production.
In order to further improve the matching degree of the classification result and the on-site actual effect, improve the accuracy of the volcanic oil reservoir productivity prediction, further improve the high efficiency and practicality of the development and deployment of the volcanic oil reservoir, and provide another specific application example of the volcanic oil reservoir productivity prediction method in combination with the volcanic oil reservoir productivity prediction method and device.
The method specifically comprises the following steps:
① The relation between the accumulated oil yield and the accumulated production time of a single well of the pseudo-alloy dragon 2 well zone good wood river volcanic oil deposit is shown as follows, wherein Q is the accumulated oil yield and ton; t-cumulative production time, day; a. b-the undetermined coefficient associated with the effective reservoir thickness. Referring to fig. 8, a corresponding single well cumulative oil production versus production time fit is obtained.
Q＝a×(1+t)b
The coefficients of the fitting relation between the cumulative production of different wells and the production time are different, and the fitting is carried out to determine the correlation coefficients a and b by combining the effective reservoir classification thickness:
a＝2.455hI+0.359hII+0.044hIII (4)
b＝0.056hI+0.036hII+0.002hIII (5)
Wherein x1, x2, and x3, and corresponding specific values 2.455, 0.359, 0.044, 0.056, 0.036, and 0.002 of y1, y2, and y3 in expressions (4) and (5) are acquired by corresponding history data; specifically, corresponding fitting determination correlation coefficients a and b are generated according to the historical accumulated production time and corresponding accumulated oil production fitting; from this fit, correlation coefficients a, b and effective reservoir thickness fits for the different wells are determined, and x1, x2, and x3, and y1, y2, and y3 are determined.
② Referring to table 1, the classification thickness of the effective reservoirs of the volcanic oil reservoirs of the Jia mu river group is shown, the effective reservoir types and the thicknesses of the different well points are combined, a single well correlation coefficient and an accumulated yield calculation formula are fitted, the annual production prediction time is taken, and finally the effective reservoir yield prediction result of the research area is calculated.
Referring to fig. 9 or table 2, the results of the annual cumulative yield prediction of the golden dragon 2 well zone and the good wood river group volcanic rock reservoir are shown, wherein the predicted yield of a single well located in a blank area in fig. 9 is 0, and the development and deployment advantage area is preferably determined. As can be seen from fig. 9 or table 2, the annual cumulative production of the single well of the Jia mu river group in the region of J214-J215 well in the north of the research area is relatively low, the prior art is not suitable for lead development, the prediction of the annual cumulative production of the single well of the Jia mu river group in the regions of J208, JL2008, J213 and JL2011 well is high, the broken blocks of the J208 well with relatively high well control degree can be deployed preferentially, and the rest of the regions are used as deployment potential regions of the next rolling test.
By the end of 2014, 6 vertical wells are drilled in the J208 well area which is arranged preferentially, 1 development control well is put into production in the beginning of 2014 in 12 months, daily oil is produced for 24.4t/d in the initial stage of production, the water content is 22%, the average daily oil production of a single well is 10.3t in 24 days, and the daily liquid production and daily oil production are stable. Therefore, the method and the device for predicting the volcanic oil reservoir productivity can quantitatively predict the volcanic oil reservoir productivity, improve the accuracy and the high efficiency of prediction, and further improve the high efficiency and the stability of volcanic oil reservoir development.
TABLE 1
TABLE 2
From the description, the volcanic oil reservoir productivity prediction method and the volcanic oil reservoir productivity prediction device provided by the application can improve the accuracy and the practicability of effective reservoir classification, improve the high efficiency and the accuracy of volcanic oil reservoir productivity prediction, provide technical support for volcanic oil reservoir development, improve the high efficiency of the development, have high practicability, save the development cost and simplify and reliable execution process.
In order to improve accuracy of volcanic oil reservoir productivity prediction and further improve efficiency of volcanic oil reservoir high-quality reservoir development, the application provides an embodiment of an electronic device for realizing all or part of content in the volcanic oil reservoir productivity prediction method, wherein the electronic device specifically comprises the following contents:
A processor (processor), a memory (memory), a communication interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete communication with each other through the bus; the communication interface is used for realizing information transmission between the volcanic oil reservoir productivity prediction device and related equipment such as a user terminal; the electronic device may be a desktop computer, a tablet computer, a mobile terminal, etc., and the embodiment is not limited thereto. In this embodiment, the electronic device may be implemented with reference to an embodiment for implementing the method for predicting the capacity of the volcanic oil reservoir and an embodiment for implementing the device for predicting the capacity of the volcanic oil reservoir, and the contents thereof are incorporated herein, and are not repeated here.
Fig. 10 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 10, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this fig. 10 is exemplary; other types of structures may also be used in addition to or in place of the structures to implement telecommunications functions or other functions.
In one or more embodiments of the application, the volcanic reservoir capacity prediction function may be integrated into the central processor 9100. The central processor 9100 may be configured to perform the following control:
Step 100: a volcanic reservoir description index for a first target well for which a valid reservoir has been identified is obtained.
Step 200: and determining the type of the effective reservoir according to the volcanic reservoir description index so as to determine the reservoir thickness of the effective reservoir according to the type of the effective reservoir.
Step 300: and obtaining two different thickness undetermined coefficients corresponding to the reservoir thickness.
Step 400: and predicting the accumulated oil yield of the first target oil well in the accumulated oil yield prediction time according to a preset accumulated oil yield prediction time and two different thickness undetermined coefficients, so as to determine the development scheme of the first target oil well based on a corresponding prediction result.
From the above description, it can be seen that the electronic device provided by the embodiment of the application can improve the accuracy of the volcanic oil reservoir yield prediction, thereby improving the high efficiency of the volcanic oil reservoir high-quality reservoir development.
In another embodiment, the volcanic reservoir capacity prediction device may be configured separately from the central processor 9100, for example, the volcanic reservoir capacity prediction device may be configured as a chip connected to the central processor 9100, and the volcanic reservoir capacity prediction function is implemented under the control of the central processor.
As shown in fig. 10, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 need not include all of the components shown in fig. 10; in addition, the electronic device 9600 may further include components not shown in fig. 10, and reference may be made to the related art.
As shown in fig. 10, the central processor 9100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, which central processor 9100 receives inputs and controls the operation of the various components of the electronic device 9600.
The memory 9140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information about failure may be stored, and a program for executing the information may be stored. And the central processor 9100 can execute the program stored in the memory 9140 to realize information storage or processing, and the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, but not limited to, an LCD display.
The memory 9140 may be a solid state memory such as Read Only Memory (ROM), random Access Memory (RAM), SIM card, etc. But also a memory which holds information even when powered down, can be selectively erased and provided with further data, an example of which is sometimes referred to as EPROM or the like. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 storing application programs and function programs or a flow for executing operations of the electronic device 9600 by the central processor 9100.
The memory 9140 may also include a data store 9143, the data store 9143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, address book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. A communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, as in the case of conventional mobile communication terminals.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, etc., may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and to receive audio input from the microphone 9132 to implement usual telecommunications functions. The audio processor 9130 can include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100 so that sound can be recorded locally through the microphone 9132 and sound stored locally can be played through the speaker 9131.
As can be seen from the above description, the electronic device provided by the embodiment of the application improves the accuracy of the volcanic oil reservoir productivity prediction, and further improves the efficiency of the volcanic oil reservoir high-quality reservoir development.
The embodiment of the present application also provides a computer-readable storage medium capable of implementing all the steps in the method for predicting the capacity of a volcanic reservoir in the above embodiment, where the computer-readable storage medium stores a computer program that, when executed by a processor, implements all the steps in the method for predicting the capacity of a volcanic reservoir in the above embodiment, for example, the processor implements the following steps when executing the computer program:
Step 100: a volcanic reservoir description index for a first target well for which a valid reservoir has been identified is obtained.
Step 200: and determining the type of the effective reservoir according to the volcanic reservoir description index so as to determine the reservoir thickness of the effective reservoir according to the type of the effective reservoir.
Step 300: and obtaining two different thickness undetermined coefficients corresponding to the reservoir thickness.
Step 400: and predicting the accumulated oil yield of the first target oil well in the accumulated oil yield prediction time according to a preset accumulated oil yield prediction time and two different thickness undetermined coefficients, so as to determine the development scheme of the first target oil well based on a corresponding prediction result.
From the above description, it can be seen that the computer readable storage medium provided by the embodiments of the present application improves accuracy of the volcanic oil reservoir productivity prediction, and further improves efficiency of volcanic oil reservoir high-quality reservoir development.
The embodiments of the method of the present application are described in a progressive manner, and the same and similar parts of the embodiments are all referred to each other, and each embodiment mainly describes differences from other embodiments. For relevance, see the description of the method embodiments.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principles and embodiments of the present application have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (6)
1. A method for predicting the capacity of a volcanic oil reservoir, comprising:
acquiring volcanic reservoir description indexes of a first target oil well with a confirmed effective reservoir;
Determining the type of the effective reservoir according to the volcanic reservoir description index so as to determine the reservoir thickness of the effective reservoir according to the type of the effective reservoir;
Obtaining two different thickness waiting coefficients corresponding to the reservoir thickness;
Predicting the accumulated oil yield of the first target oil well in the accumulated oil yield prediction time according to a preset accumulated oil yield prediction time and two different thickness undetermined coefficients, so as to determine a development scheme of the first target oil well based on a corresponding prediction result;
Prior to the obtaining the volcanic reservoir description indicator for the first target well for which the presence of a valid reservoir has been confirmed, further comprising:
Acquiring the reservoir thickness of each effective reservoir of a second target oil well, a plurality of accumulated production times and accumulated oil production in the accumulated production time, wherein the second target oil well and the first target oil well are in the same preset area;
Acquiring reservoir thicknesses of effective reservoirs of a plurality of oil wells in the preset area;
Fitting a plurality of accumulated production times of the second target oil well and accumulated oil production in the accumulated production time to obtain a first historical thickness pending coefficient and a second historical thickness pending coefficient;
fitting the first historical thickness undetermined coefficient and reservoir thicknesses of the effective reservoirs of the plurality of oil wells to obtain a first fitting undetermined coefficient group;
Fitting the second historical thickness undetermined coefficient with reservoir thicknesses of the effective reservoirs of the plurality of oil wells to obtain a second fitting undetermined coefficient group;
the obtaining two different thickness pending coefficients corresponding to the reservoir thickness includes:
and obtaining a first thickness undetermined coefficient through the reservoir thickness and the first fitting undetermined coefficient group, and obtaining a second thickness undetermined coefficient through the reservoir thickness and the second fitting undetermined coefficient group.
2. The method of claim 1, wherein the volcanic reservoir capacity prediction index comprises:
an oil reservoir quality factor and a fracture density, wherein the oil reservoir quality factor comprises: reservoir permeability and porosity;
correspondingly, the determining the type of the effective reservoir according to the volcanic reservoir description index comprises the following steps:
Acquiring corresponding classification discrimination parameters according to the oil reservoir quality factors and the fracture density;
normalizing the classification discrimination parameters, and determining the type of the effective reservoir according to the normalized classification discrimination parameters, wherein the type of the effective reservoir comprises: one type of effective reservoir, two types of effective reservoirs and three types of effective reservoirs.
3. A volcanic reservoir capacity prediction apparatus, comprising:
the first acquisition module is used for acquiring volcanic reservoir descriptive indexes of a first target oil well with the confirmed effective reservoir;
The judging module is used for determining the type of the effective reservoir according to the volcanic reservoir description index so as to determine the reservoir thickness of the effective reservoir according to the type of the effective reservoir;
The second acquisition module is used for acquiring two different thickness waiting coefficients corresponding to the reservoir thickness;
The prediction module is used for predicting the accumulated oil yield of the first target oil well in the accumulated oil yield prediction time according to the preset accumulated oil yield prediction time and two different thickness undetermined coefficients so as to determine a development scheme of the first target oil well based on a corresponding prediction result;
The volcanic oil reservoir productivity prediction device further comprises:
The historical data acquisition module is used for acquiring the reservoir thickness of each effective reservoir of the second target oil well, a plurality of accumulated production time and accumulated oil production in the accumulated production time, wherein the second target oil well and the first target oil well are in the same preset area;
the third acquisition module is used for acquiring the reservoir thicknesses of the effective reservoirs of the plurality of oil wells in the preset area;
The first fitting module is used for fitting a plurality of accumulated production times of the second target oil well and accumulated oil production in the accumulated production time to obtain a first historical thickness undetermined coefficient and a second historical thickness undetermined coefficient;
The second fitting module is used for fitting the first historical thickness undetermined coefficient and reservoir thicknesses of the effective reservoirs of the plurality of oil wells to obtain a first fitting undetermined coefficient group;
a third fitting module, configured to fit the second historical thickness coefficient to reservoir thicknesses of each of the plurality of effective reservoirs of the oil well to obtain a second set of fitted coefficient coefficients;
The second acquisition module further comprises:
and the undetermined coefficient acquisition unit is used for acquiring a first thickness undetermined coefficient through the reservoir thickness and the first fitting undetermined coefficient group and acquiring a second thickness undetermined coefficient through the reservoir thickness and the second fitting undetermined coefficient group.
4. The volcanic reservoir capacity prediction apparatus as claimed in claim 3, wherein said volcanic reservoir description index comprises:
an oil reservoir quality factor and a fracture density, wherein the oil reservoir quality factor comprises: reservoir permeability and porosity;
correspondingly, the judging module comprises:
The classification discrimination parameter obtaining unit is used for obtaining corresponding classification discrimination parameters according to the oil reservoir quality factors and the fracture density;
The normalization unit is used for carrying out normalization processing on the classification discrimination parameters and determining the type of the effective reservoir according to the classification discrimination parameters after normalization processing, wherein the type of the effective reservoir comprises: one type of effective reservoir, two types of effective reservoirs and three types of effective reservoirs.
5. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor performs the steps of the volcanic reservoir capacity prediction method of claim 1 or 2.
6. A computer readable storage medium having stored thereon computer instructions, which when executed by a processor, implement the steps of the volcanic reservoir capacity prediction method of claim 1 or 2.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104695950A (en) * | 2014-10-31 | 2015-06-10 | 中国石油集团西部钻探工程有限公司 | Prediction method for volcanic rock oil reservoir productivity |
| CN104929626A (en) * | 2015-06-02 | 2015-09-23 | 中国石油大学(华东) | Method for identifying lithologic characters of oil reservoirs of carboniferous volcanic rock |
| CN106295095A (en) * | 2015-05-15 | 2017-01-04 | 中国石油化工股份有限公司 | New method based on Conventional Logs prediction low permeability sandstone reservoir production capacity |
| CN107965318A (en) * | 2017-12-06 | 2018-04-27 | 中国石油天然气股份有限公司 | A kind of method of Volcanic Reservoir effective reservoir quantitative classification |
| CN109958430A (en) * | 2019-03-10 | 2019-07-02 | 东北石油大学 | Complicated tight gas reservoir PRODUCTION FORECASTING METHODS |
-
2019
- 2019-09-23 CN CN201910897978.5A patent/CN112541607B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104695950A (en) * | 2014-10-31 | 2015-06-10 | 中国石油集团西部钻探工程有限公司 | Prediction method for volcanic rock oil reservoir productivity |
| CN106295095A (en) * | 2015-05-15 | 2017-01-04 | 中国石油化工股份有限公司 | New method based on Conventional Logs prediction low permeability sandstone reservoir production capacity |
| CN104929626A (en) * | 2015-06-02 | 2015-09-23 | 中国石油大学(华东) | Method for identifying lithologic characters of oil reservoirs of carboniferous volcanic rock |
| CN107965318A (en) * | 2017-12-06 | 2018-04-27 | 中国石油天然气股份有限公司 | A kind of method of Volcanic Reservoir effective reservoir quantitative classification |
| CN109958430A (en) * | 2019-03-10 | 2019-07-02 | 东北石油大学 | Complicated tight gas reservoir PRODUCTION FORECASTING METHODS |
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