Application of Machine Learning in Well Performance Prediction, Design Optimization and History Matching

Finite difference based reservoir simulation is commonly used to predict well rates in these reservoirs. Such detailed simulation requires an accurate knowledge of reservoir geology. Also, these reservoir simulations may be very costly in terms of computational time. Recently, some studies have used the concept of machine learning to predict mean or maximum production rates for new wells by utilizing available well production and completion data in a given field. However, these studies cannot predict well rates as a function of time. This dissertation tries to fill this gap by successfully applying various machine learning algorithms to predict well decline rates as a function of time. This is achieved by utilizing available multiple well data (well production, completion and location data) to build machine learning models for making rate decline predictions for the new wells. It is concluded from this study that well completion and location variables can be successfully correlated to decline curve model parameters and Estimated Ultimate Recovery (EUR) with a reasonable accuracy. Among the various machine learning models studied, the Support Vector Machine (SVM) algorithm in conjunction with the Stretched Exponential Decline Model (SEDM) was concluded to be the best predictor for well rate decline. This machine learning method is very fast compared to reservoir simulation and does not require a detailed reservoir information. Also, this method can be used to fast predict rate declines for more than one well at the same time. This dissertation also investigates the problem of hydraulic fracture design optimization in unconventional reservoirs. Previous studies have concentrated mainly on optimizing hydraulic fractures in a given permeability field which may not be accurately known. Also, these studies do not take into account the trade-off between the revenue generated from a given fracture design and the cost involved in having that design. This dissertation study fills these gaps by utilizing a Genetic Algorithm (GA) based workflow which can find the most suitable fracturing design (fracture locations, half-lengths and widths) for a given unconventional reservoir by maximizing the Net Present Value (NPV). It is concluded that this method can optimize hydraulic fracture placement in the presence of natural fracture/permeability uncertainty. It is also concluded that this method results in a much higher NPV compared to an equally spaced hydraulic fractures with uniform fracture dimensions. Another problem under investigation in this dissertation is that of field scale history matching in unconventional shale oil reservoirs. Stochastic optimization methods are commonly used in history matching problems requiring a large number of forward simulations due to the presence of a number of uncertain variables with unrefined variable ranges. Previous studies commonly used a single stage history matching. This study presents a method utilizing multiple stages of GA. Most significant variables are separated out from the rest of the variables in the first GA stage. Next, best models with refined variable ranges are utilized with previously eliminated variables to conduct GA for next stage. This method results in faster convergence of the problem