Optimization options modeling for decision-making while oil and gas development monitoring and control
Currently, the relevant issue is development of an effective management and monitoring system of an oilfield development, which requires use of hydrodynamic models (HM) for. This paper describes the main stages of development of some permanently applied geological-technological model (PGTM) and analyzes of the data required for its development. Some method of application of artificial neural networks and procedure of their training to adjust the geological-technological model was studied. An algorithm and a sequence of the neural network training for solving the optimization problem of production and injection wells with minimization of the mean square error during the training process are proposed. It was revealed that the method under consideration allows predicting the volume of the injected fluid and optimizing operational modes of injection and production wells. The conclusion about the importance of the problem of fields’ intellectualization, based on wells’ equipment by measurement devices and microprocessor units of automation, allowing control of wells and oilfields in real time is also made.