Parallel Automatic History Matching Algorithm Using Reinforcement Learning

Optimally developing an oil and gas field requires predicting future production using a reservoir model, whose key material properties are tuned in a process called history matching. This process of adjusting the key parameters is non-unique and computationally challenging. Typically, the reservoir model is divided into cells that match the geology of the field. The key properties of these cells, such as porosity and permeability, are assigned initially using core sample data, where available. For computational efficiency, the geological model is converted to a reservoir model using upscaling [6, 20, 49] to reduce the number of the cells in the model. Due to the challenges of finding the key properties in each cell, history matching is used to adjust the values of these properties so the model reflects historical production data [19, 28, 9]. History matching is typically done by matching the computed pressure and saturation data (oil, gas and water rates) from the simulation model and comparing it the actual historical data. The difference between the actual data and data generated by the reservoir model is then computed using an objective function that quantifies the mismatch between the two quantities.