10 results about "Reservoir modeling" patented technology

The embodiment of the invention provides a thin interlayer and phase control physical property cause sand body configuration model construction method and system, and belongs to the technical field of reservoir modeling. The method comprises the following steps: identifying fluvial facies reservoir distribution characteristics based on exploration data, and constructing a corresponding cause sand body reservoir configuration model; obtaining a corresponding small-layer sand body deterministic model based on the cause sand body reservoir configuration model; on the basis of the deterministic model of the small-layer sand body, performing representation of an internal configuration interface of the sand body based on a lateral lamination model and an adaptive triangular mesh generation algorithm; and generating a distribution model of each lithofacies unit based on representation of a configuration interface in the sand body, and constructing a physical property model for fine modeling and heterogeneity description of high and low permeability sections in the configuration based on a phase control stochastic simulation method. According to the scheme, the limitations of insufficient precision, unreasonable transition and low calculation efficiency in traditional modeling are effectively overcome, and high-precision geologic model support is provided for reservoir management and oil reservoir development.

The application discloses a reservoir intelligent inversion method and related device, relates to the technical field of reservoir intelligent inversion, and comprises the following steps: obtaining multi-source heterogeneous data of a target area and preprocessing; constructing an initial geological model generator based on a conditional diffusion model, and optimizing hidden space parameters in a manner of seismic wave forward simulation; constructing a reservoir knowledge graph and adaptively pretraining and migrating the model online; performing task demand analysis, task decomposition and dynamic collaborative execution on a reservoir modeling task based on a thinking chain technology and a language large model technology, and obtaining iteratively optimized hidden space parameters; inputting the iteratively optimized hidden space parameters into the optimized geological model generator, outputting a three-dimensional distribution model, and extracting a confidence interval feature; incrementally updating the reservoir knowledge graph, determining a reservoir model conforming to a geological mode feature and matching a seismic waveform, and realizing reservoir intelligent inversion. The application can improve the accuracy and efficiency of reservoir inversion.

This invention relates to the field of oil and gas reservoir modeling technology, and discloses a method for real-time calibration and trend prediction of oil and gas reservoir models that integrates dynamic production data. The invention aims to solve the technical problem that the prediction accuracy of existing reservoir models gradually decreases with changes in oilfield production dynamics after a one-time calibration, and lacks an automated, continuous correction mechanism. The method involves initial calibration of the reservoir model and generation of a baseline prediction; continuous monitoring of the deviation between actual production data and the baseline prediction; automatic calibration of the reservoir model to reduce the deviation when the deviation exceeds a preset threshold; and generation of a new production trend prediction based on the calibrated model, which replaces the baseline prediction, forming a closed loop. This invention enables the reservoir model to dynamically reflect production changes, achieves automated and rolling updates of model parameters, and provides a way to generate probabilistic prediction results, improving the timeliness of predictions and the reliability of decision support.

The application provides a three-dimensional reservoir modeling method based on semantic rule guidance and multi-point geostatistics, relates to the geological field, and the method comprises the following steps: arranging strata in reverse order according to deposition time, iteratively executing a finite difference interpolation algorithm from the newest stratum, and taking the current stratum as a normal vector constraint of the next stratum; quantifying the constraint of fault kinematics on the surrounding deformation zone; under the support of an implicit structure model, adopting a two-dimensional parameter optimization strategy to dynamically configure the parameters of a fast sampling algorithm; adaptively matching the local structure features of a training image through a kernel function, and globally searching for an optimal combination in a grid; finally, taking a structural model as a spatial boundary constraint, performing collaborative simulation of lithofacies parameters, generating an attribute field according to the quantile sampling rule of Euclidean distance and Hamming distance, until all grid nodes are simulated and a three-dimensional reservoir model is output. The technical problem of collaborative fusion of multi-source heterogeneous data and geological semantic rules in a complex heterogeneous reservoir is effectively solved.

The application discloses a multi-layered shale reservoir automatic modeling method based on bedding identification, specifically comprising the following steps: obtaining core samples from a shale reservoir, recording interval information, and obtaining core images; carrying out denoising processing on the core images and carrying out contrast enhancement; using a Canny edge detection algorithm to identify bedding edges of the core images; using a Hough line transformation algorithm to detect straight lines and identify bedding structure lines; judging whether the lines are straight by calculating the change of adjacent pixel points on the y-axis, calculating the depth of the lines according to the pixel coordinates of the lines; creating planes representing different beddings according to the depth values of each bedding, generating color of brightness and darkness, and further obtaining a shale multi-layered reservoir model. The automatic modeling method significantly improves the accuracy and efficiency of shale reservoir modeling, accurately identifies shale bedding characteristics by combining the bedding identification technology, and provides more detailed and reliable basic data for reservoir modeling.

A method for generating one or more reservoir models using machine learning is provided. Generating reservoir models is typically a time-intensive idiosyncratic process. However, machine learning may be used to generate one or more reservoir models that characterize the subsurface. The machine learning may use geological data, geological concepts, reservoir stratigraphic configurations, and one or more input geological models in order to generate the one or more reservoir models. As one example, a generative adversarial network (GAN) may be used as the machine learning methodology. The GAN includes two neural networks, including a generative network (which generates candidate reservoir models) and a discriminative network (which evaluates the candidate reservoir models), contest with each other in order to generate the reservoir models.

The application discloses a river deposition simulation and data acquisition integrated device for oil and gas reservoir prediction, and relates to the field of petroleum geology.The device comprises a central control unit, wherein the central control unit comprises a water power control system, a sand supply and deposition system, a river evolution observation and data acquisition system, a digital control and analysis system and an oil and gas reservoir prediction coupling module.The river deposition simulation and data acquisition integrated device for oil and gas reservoir prediction has high control precision, can realize linkage regulation and control of multiple parameters such as flow velocity, slope, sand supply amount and water level, can accurately simulate the dynamic process of river deposition under controllable conditions, can collect fluid parameter and deposition form data in real time, and can realize quantitative correlation from the deposition experiment to the reservoir structure prediction by combining digital processing with reservoir modeling technology, thereby providing reliable experimental basis and data support for oil and gas geological research and reservoir prediction.

The application discloses a kind of fracture-cave carbonate gas reservoir modeling methods, it is related to carbonate rock oil and gas geology and development technical field, solve the simple, lack of systematicness of existing fracture-cave modeling method;The simulation result of cave and fracture is not reasonable enough, and then it is difficult to be applied to guide gas field exploration and development and the like problem, the method of the present application establishes fine structure model comprehensively stratum correlation and seismic interpretation result.On the basis of the fine structure model, matrix attribute model, fracture model and cave model are established, and finally a dual medium model is constructed through the matrix attribute model, the fracture model and the cave model.The method of the present application establishes a fine geological model to depict the distribution space of the preferred reservoir based on the integration of the distribution patterns of matrix, fractures and caves. By analyzing the controlling factors of fractures and caves and establishing a fracture-cave model, more accurate decision-making basis can be provided for gas field development, and the exploration and development of gas fields can be guided.