Search

CN-121787127-B - Method for grading and using residual oil through joint driving of potential energy gradient field and well seam

CN121787127BCN 121787127 BCN121787127 BCN 121787127BCN-121787127-B

Abstract

The invention discloses a method for grading and using residual oil based on joint driving of a potential energy gradient field and well gap communication, and relates to the technical field of oil and gas field development. According to the invention, an oil reservoir numerical model is established in an oil reservoir numerical simulator, production history data fitting is carried out on simulation data of the oil reservoir numerical model, the oil reservoir numerical model is adjusted, key field distribution conditions of an oil reservoir are obtained, then a crude oil migration process is quantitatively described according to fluid properties of the oil reservoir, driving force and retention force are introduced, a crude oil displacement potential energy model is established, the crude oil displacement potential energy model is coupled with the oil reservoir numerical model, potential energy gradient data of the oil reservoir and well seam connectivity coefficient data are obtained, a plurality of oil reservoir feature vectors are obtained after pretreatment, an oil reservoir feature data set is established, double-parameter clustering analysis is carried out based on a K-means clustering algorithm, classification results of all oil reservoir grids are obtained, the utilization grades of the oil reservoir grids are determined, and accurate acquisition of residual oil distribution and scientific grading of utilization potential are realized.

Inventors

  • YUAN BIN
  • SONG WENTONG
  • ZHANG WEI

Assignees

  • 中国石油大学(华东)

Dates

Publication Date
20260508
Application Date
20260303

Claims (8)

  1. 1. The method for grading and using the residual oil based on the joint driving of the potential energy gradient field and the well gap is characterized by comprising the following steps of: step 1, drilling, logging and geological data of an oil reservoir are synthesized, and an oil reservoir numerical model is built in an oil reservoir numerical simulator; Step 2, carrying out numerical simulation by utilizing an oil reservoir numerical model according to the production test data and the fracturing construction monitoring data of the oil reservoir, obtaining oil reservoir production simulation data, fitting with actual production historical data of the oil reservoir, adjusting the oil reservoir numerical model until the coincidence degree between the oil reservoir production simulation data obtained by simulation and the actual production historical data reaches a preset requirement, and obtaining the key field distribution condition of the oil reservoir by utilizing the adjusted oil reservoir numerical model; Step 3, quantitatively describing a crude oil migration process according to the property of fluid in an oil reservoir, introducing driving force and retention force, and establishing a crude oil displacement potential energy model; Step 4, acquiring a full-area potential energy distribution field of the oil reservoir based on the crude oil displacement potential energy model and the adjusted oil reservoir numerical model, calculating potential energy gradients at all positions of the oil reservoir through space derivation, and acquiring potential energy gradient fields of the oil reservoir to obtain potential energy gradient data of the oil reservoir; step 5, based on a streamline simulation method, simulating by using the adjusted oil reservoir numerical model to obtain the overall streamline distribution condition in the oil reservoir, and calculating the connectivity coefficient of each oil reservoir grid in the oil reservoir numerical model according to the fluid flux to obtain the well gap connectivity coefficient data of the oil reservoir; step 6, preprocessing potential energy gradient data and well fracture connectivity coefficient data of an oil reservoir to obtain a plurality of oil reservoir feature vectors, establishing an oil reservoir feature data set, and carrying out double-parameter cluster analysis based on a K-means clustering algorithm to obtain a classification result of each oil reservoir grid in an oil reservoir numerical model; Step 7, obtaining residual oil mining potential values at all areas of the oil deposit according to classification results of all oil deposit grids in the oil deposit numerical model, and determining the utilization level at all areas of the oil deposit by combining preset residual oil grading utilization standards; The crude oil displacement potential energy model comprises energy, kinetic energy, interface energy, viscous resistance energy and flow resistance energy, wherein the pressure energy and the kinetic energy are crude oil driving force energy, and the interface energy, the viscous force energy and the flow resistance energy are crude oil retention force energy; The crude oil displacement potential energy model is set as follows: ; Wherein, the ; ; ; ; ; In the formula, Displacement potential energy for crude oil; Is pressure energy; The current pressure of the oil phase; initial pressure for oil phase; Is the relative permeability of the oil phase; is the dynamic viscosity of crude oil; calculating a function for the oil phase density; is the formation pressure; Is kinetic energy; calculating a function for the oil phase velocity; is interface force energy; is the interfacial tension of the oil phase; is the wetting angle between the oil phase and the rock; is the capillary radius of the rock pore; is viscous force energy; A migration velocity gradient for the oil phase; Is the flow resistance energy; in order to provide a coefficient of resistance along the way, , A Reynolds number for the oil phase migration; is the crack length; is the crack width.
  2. 2. The method for grading residual oil based on joint driving of potential gradient fields and well fractures according to claim 1 is characterized in that in the step 1, a construction grid of a three-dimensional geological model is built according to well tracks, layering and fault data of an oil reservoir, a phase control model in the construction grid is set according to conventional well logging curves, imaging logging data and oil reservoir fine description results to obtain the three-dimensional geological model for simulating spatial distribution of porosity, permeability and fluid saturation in the oil reservoir, a reservoir fluid model is set according to fluid attribute parameters of the oil reservoir, the reservoir fluid model is embedded into the three-dimensional geological model, and an oil reservoir numerical model is built for simulating oil reservoir production test data.
  3. 3. The method for grading residual oil based on joint driving of potential gradient fields and well fracture communication according to claim 1, wherein in the step 2, an oil reservoir numerical model is adjusted according to actual production history data of an oil reservoir, so that the coincidence degree between oil reservoir production simulation data obtained by simulating the oil reservoir numerical model and actual production history data is not lower than 95%, and the coincidence degree between fracture expansion form, fracture network volume and seepage capacity of the oil reservoir numerical model after fracturing and oil reservoir fracturing construction monitoring data is not lower than 95%, so that the key field distribution situation of the oil reservoir including an oil saturation field, a pressure field, a fluid velocity field, an interface tension field, a viscous resistance field and a flow resistance field is obtained by simulating by using the adjusted oil reservoir numerical model.
  4. 4. The method for grading and using residual oil based on joint driving of potential gradient fields and well fractures according to claim 1, wherein in the step 4, key field parameters of each position of an oil reservoir obtained by simulating an oil reservoir numerical model are substituted into a crude oil displacement potential energy model, potential energy of each oil reservoir grid in the oil reservoir numerical model is obtained by calculating the crude oil displacement potential energy model, potential energy distribution of a whole area of the oil reservoir is obtained, potential energy gradients of each position of the oil reservoir numerical model are obtained by conducting space derivation, the oil reservoir potential energy gradient fields are established, and potential energy gradient data of the oil reservoir are obtained; The reservoir potential energy gradient field is expressed as: ; ; Wherein, the ; ; ; In the formula, Is the position coordinates of the reservoir grid in the reservoir numerical model, Is the abscissa of the reservoir grid, Is the ordinate of the reservoir grid, Vertical coordinates of the reservoir grid; for oil reservoir grids in a numerical model of oil reservoirs A potential energy vector field at the location; Is a del operator; for oil reservoir grids in a numerical model of oil reservoirs A potential energy scalar field at; Calculating for modulus; 、 、 respectively the potential energy gradients are Direction(s), Direction(s), A component in the direction; 、 、 respectively, potential energy scalar field is Direction(s), Direction(s), Partial derivative in direction; 、 、 Respectively is Direction(s), Direction(s), A basis vector in the direction; 、 、 Grid in adjacent oil reservoirs Direction(s), Direction(s), Spacing in the direction; for oil reservoir grids in a numerical model of oil reservoirs A scalar field of potential energy at the location, For oil reservoir grids in a numerical model of oil reservoirs A potential energy scalar field at; for oil reservoir grids in a numerical model of oil reservoirs A scalar field of potential energy at the location, For oil reservoir grids in a numerical model of oil reservoirs A potential energy scalar field at; for oil reservoir grids in a numerical model of oil reservoirs A scalar field of potential energy at the location, For oil reservoir grids in a numerical model of oil reservoirs Potential energy scalar field at the site, reservoir grid And reservoir grid And oil reservoir grid At the position of Adjacent in direction, oil reservoir grid And reservoir grid And oil reservoir grid At the position of Adjacent in direction, oil reservoir grid And reservoir grid And oil reservoir grid At the position of Adjacent in direction.
  5. 5. The method for grading residual oil based on joint driving of potential gradient field and well gap communication according to claim 1, wherein in the step 5, the connectivity coefficient calculation formula is as follows: ; in the formula, Is the first Connectivity coefficients of the individual reservoir grids; is a streamline serial number; Is the total number of streamline; Is the first Volumetric flow carried by the strip-flow line; as a streamline indicating function, when The strip streamline passes through the first In the case of a grid of individual reservoirs, The value is 1, otherwise, The value is 0.
  6. 6. The method for classifying residual oil based on joint driving of potential gradient field and well gap communication according to claim 1, wherein in step 6, firstly, homogenizing potential gradient data and well gap connectivity coefficient data of an oil reservoir respectively so that values of all potential gradient data and well gap connectivity coefficient data are located Obtaining oil deposit feature vectors of all oil deposit grids according to potential energy gradients and well fracture connectivity coefficients of all oil deposit networks in the oil deposit numerical model, and establishing an oil deposit feature data set according to the oil deposit feature vectors of all the oil deposit grids in the oil deposit numerical model as sample data points; Partitioning a reservoir characterization dataset into Clustering to obtain The clustering centers take the minimization of the square sum of the distances between all sample data points and the clustering center to which the sample data points belong as an optimization target, and an objective function is constructed The method comprises the following steps of: ; in the formula, Is a cluster sequence number; Is the total number of clusters; Is the first The feature vectors of the individual reservoirs are used, , wherein, Is the first The potential energy gradient of the individual reservoir grids, Is the first The connectivity coefficients of the individual reservoir grids, Is a transposed matrix; Is the first A cluster set; Is the first Coordinate values of the clustering centers in potential energy gradient dimension; Is the first Coordinate values of the clustering centers in the connectivity coefficient dimension; Optimizing an objective function based on a K-means clustering algorithm, determining the clustering center of each current cluster in each optimization process, traversing all oil reservoir grids in an oil reservoir numerical model, dividing each oil reservoir grid into a cluster set to which the clustering center with the smallest Euclidean distance square belongs, and forming And (3) temporary clustering, namely acquiring the mass center of each temporary cluster and taking the mass center as a cluster center, and stopping optimizing the objective function when the cluster center of each temporary cluster meets a preset optimization ending judgment standard, so as to obtain the classification result of each oil reservoir grid in the oil reservoir numerical model.
  7. 7. The method for grading residual oil based on joint driving of potential gradient fields and well joints according to claim 6, wherein the optimization ending criterion is set based on two principles of local optimal solution and engineering protection, and when the clustering center of each temporary cluster is not changed or the variation is smaller than a preset minimum tolerance threshold value or the current optimization times reach a preset maximum optimization times after two continuous optimizations, the preset optimization ending criterion is met at the moment, the cluster classification of all sample data points in the oil reservoir characteristic data is completed, and the optimization of the objective function is stopped.
  8. 8. The method for grading the residual oil based on the joint driving of the potential gradient field and the well fracture according to claim 1, wherein in the step 7, the internal area of the oil reservoir is divided into a primary area, a secondary area, a tertiary area and a quaternary area according to the residual oil excavation potential value, wherein when the residual oil excavation potential value of the oil reservoir area is greater than 0.8 and not more than 1, the oil reservoir area is judged to be the primary area, when the residual oil excavation potential value of the oil reservoir area is greater than 0.6 and not more than 0.8, the oil reservoir area is judged to be the secondary area, when the residual oil excavation potential value of the oil reservoir area is greater than 0.4 and not more than 0.6, the oil reservoir area is judged to be the tertiary area, and when the residual oil excavation potential value of the oil reservoir area is greater than 0 and not more than 0.4, the oil reservoir area is judged to be the quaternary area.

Description

Method for grading and using residual oil through joint driving of potential energy gradient field and well seam Technical Field The invention relates to the technical field of oil and gas field development, in particular to a method for grading and using residual oil based on joint driving of a potential energy gradient field and well gap communication. Background Along with the continuous increase of oil and gas resource demands, most oil and gas fields gradually enter the middle and later stages of development, and the mining center of gravity gradually turns to development, adjustment and diving. Considering that a complex reservoir is influenced by formation heterogeneity and development measures such as long-term water flooding, gas flooding and fracturing in the development process, a large amount of residual oil still remains in the reservoir, and the difficulty in utilization is remarkably increased along with the gradual exploitation of oil and gas resources and the gradual dispersion of the distribution of the residual oil. Therefore, accurately characterizing the spatial distribution of the remaining oil and effectively utilizing the spatial distribution of the remaining oil has become a core challenge for stable production and enhanced recovery of current oil and gas fields. The distribution of the residual oil is controlled by multiple factors such as geological structure, sedimentary microphase, development history and the like, and the characteristics of strong heterogeneity and dynamic evolution are presented, so that the problems of low precision, insufficient reliability and the like of the traditional residual oil distribution prediction method are caused. In addition, the prior art focuses on single parameters such as the saturation of the residual oil or the abundance of the oil, so that the distribution rule of the residual oil under multi-factor coupling is difficult to reflect comprehensively, the pertinence of development and adjustment measures is severely restricted, and the overall development benefit and resource connection capability of the oil-gas field are affected. At present, oil field residual oil analysis and research is mainly developed around four methods, namely a static geological analysis method, a production dynamic analysis method, an oil reservoir fine description method and an oil reservoir numerical simulation method, wherein the static geological analysis method mainly relies on geological static data such as a drilling core, a logging and the like, residual oil saturation of a reservoir is measured through experiments, and distribution of local residual oil is depicted. The production dynamic analysis method is based on oilfield production data, combines a substance balance principle and dynamic response characteristics, deduces macroscopic distribution and development potential of the residual oil, has good instantaneity and global view, but is generally not unique in solution and difficult to finely characterize the spatial distribution details of the residual oil. The oil reservoir fine description rule integrates multi-source information such as geology, geophysics, well logging, testing and the like, semi-quantitative and quantitative description is carried out through reservoir modeling and geostatistical means, but the accuracy is excessively dependent on expert experience and subjective judgment, and repeatability and objectivity are to be improved. The oil reservoir numerical simulation method integrates the advantages of the method, the three-dimensional geological model is constructed, history fitting is carried out, the oil reservoir parameter is inverted to predict the residual oil distribution, and oil reservoir development and adjustment are guided, but the method has extremely high requirements on the history fitting accuracy, is limited by the calculation efficiency and the model complexity, and still faces great challenges in practical application. In summary, the existing oil field residual oil analysis and research method has limitations of different degrees in terms of precision, cost, efficiency and applicability, and restricts the efficient oil extraction of residual oil. Therefore, it is needed to propose a method for grading residual oil based on joint driving of potential energy gradient fields and well joints, which integrates multidisciplinary data and has accuracy, economy and operability, so as to realize accurate acquisition of residual oil distribution and scientific grading of the utilization potential. Disclosure of Invention The invention aims to solve the problems, and provides a potential energy gradient field and well fracture communication combined driving-based residual oil grading and utilization method, which realizes accurate quantitative prediction of residual oil distribution and scientific grading of utilization potential, effectively guides the utilization potential of residual oil in each stage of an oil field a