The objective of this study is to develop a hybrid model by combining physics and data-driven approach for unconventional field development planning. We used physics-based reservoir simulations to generate training datasets. These uncalibrated priors were incorporated into data-driven machine learning (ML) algorithms so that the algorithms can learn the underlying physics from reservoir simulation input and output. The ML model is trained such that it provides fast and scalable applications with good accuracy to find optimum unconventional field development, accounting for geological properties, completions design, well spacing and child well timing.
We trained ML models with reservoir simulation inputs and cumulative oil production for parent and child wells. A single half-cluster reservoir model was built where fracture propagation is simulated with pressure-dependent fracture properties and a child-well is introduced with different timing and well spacing. After performing a sensitivity analysis to reduce the number of training inputs, more than 20,000 simulations results were generated as the training data. The best accuracy, R2=0.94, was achieved with the neural network model after tuning hyper-parameters. Then, we incorporated the trained model with the genetic algorithm to perform efficient history matching to calibrate model parameters.
The hybrid model, physics-embedded machine learning model, is extremely efficient that it takes several minutes to complete a single well history matching. The prediction from the history-matched hybrid model is physically meaningful showing that it properly captures the impact of fracture geometry, child well spacing, and timing on production. With the multiple history matching results, we populated spatial distribution of estimated ultimate recovery (EUR) and calibrated model parameters. To validate the workflow, a blind test was conducted on selected areas from US onshore field. The model prediction with the populated parameters was found to be in good agreement with the actual production history indicating the predictive capability of the hybrid approach.
The proposed model can provide quick and scalable solutions that honors underlying physics to help decision making on unconventional field development. The model can capture interactions between wells including production degradation due to child-well effect. By calibrating model input parameters over the entire basin, we can predict EUR, yearly cumulative oil followed by economic metrics such as NPV10 at any location in the basin. The impact of different completion design (e.g., fluid intensity, cluster spacing) on production profile and economic matrices is also quickly assessed.