Abstract: The invention relates to the technical field of in-situ development of shale oil resources and consists of a device and technique for simulating the propagation of shale fractures under the influence of high-temperature convective heat. The apparatus is comprised of a data collecting and processing system, a high-temperature thermal fluid generator, a high-pressure pumping device, and a shale reactor. The thermal fluid generator for high temperatures consists of a fluid generator, a temperature controller, and a pressure controller. The shale, reaction kettle consists of an outer chamber, an outer chamber lid, a scaled rock cavity, and a shale sample. The outer cavity cover of the reactor is fitted with a simulated wellbore, the bottom end of the simulated wellbore penetrates the inner cavity cover and extends to the interior of the shale sample, and the top of the simulated wellbore is connected to a high-pressure constant-speed injection pump.

Abstract: The invention provides a collaborative optimization method for gas injection huff-n-puff parameters in tight oil reservoirs. The method relates to the technical field of oilfield development parameter optimization, including: (1) establishing the numerical simulation model that accurately describes the actual oil reservoirs; (2) determining the optimization parameters of gas injection huff-n-puff, giving the optimization range of gas injection huff-n-puff parameters and other variable constraints, and establishing an optimization objective function; and (3) using the particle swarm optimization algorithm to solve the objective function constructed based on a collaborative optimization model for gas injection huff-n-puff parameters. Then the optimal gas injection rate, gas injection time, soaking time, and production time after the collaborative optimization for gas injection huff-n-puff parameters of the reservoir are obtained.

Abstract: The present invention relates to an automatic history matching system for an oil reservoir based on transfer learning, comprising a data reading module, a population reinitializing module, an optimization module, a simulated calculation module, a comparative judgment module and an output module, wherein the data reading module reads an optimized result of an existing oil reservoir, outputs the optimized result to the population reinitializing module, obtains an initial population of a new oil reservoir by calculation and outputs the initial population to the optimization module; the optimized result is outputted to the simulated calculation module to obtain oil reservoir production simulated data, and the oil reservoir production simulated data is outputted to the comparative judgment module; when an error between the simulated data and observed data meets the requirement, the optimized result is outputted to the output module, and the system operation is completed; and if the error does not meet the requirem

Abstract: The present invention relates to an injection-production relationship optimization method based on heterogeneous flow field characterization. The method comprises the steps of: first, calculating a density of a flow field by adopting a method of converting a linear density into a dot density; then, calculating an intensity of the flow field by adopting an analytic hierarchy process; and performing flow field characterization by utilizing mathematical methods such as PCA dimensionality reduction and clustering and calculating a product of flow line densities and intensities of flow fields in different regions of the flow field, and performing optimization for a goal of minimizing a variance of the product in combination of an genetic algorithm to solve the optimum injection-production quantity as an optimum solution (the optimum injection-production quantity) that enables the flow field to be displaced in a balanced manner.

Abstract: The present disclosure relates to a parallel proxy model based machine learning method for oil reservoir production. With the proposed method, multiple optimized candidate solutions can be obtained within an iteration, and then a matrix laboratory (e.g., MATLAB) is used to call numerical simulation software Eclipse in parallel to conduct actual evaluation on the candidate solutions simultaneously, so that optimization time of complex problems can be greatly reduced. With the method of the present disclosure, the efficiency of solving an oilfield production optimization problem can be speeded up to a greater extent than in the art, and the final optimization effect can be improved. Moreover, the method of the present disclosure may further be used for well pattern optimization, history matching, and so on, apart from adjusting schedules of the producers and injectors in the oilfield.