Abstract
Machine learning (ML) approaches have risen in popularity for use in many oil and gas (O&G) applications. Time series-based predictive forecasting of hydrocarbon production using deep learning ML strategies that can generalize temporal or sequence-based information within data is fast gaining traction. The recent emphasis on hydrocarbon production provides opportunities to explore the use of deep learning ML to other facets of O&G development where dynamic, temporal dependencies exist and that also hold implications to production forecasting. This study proposes a combination of supervised and unsupervised ML approaches as part of a framework for the joint prediction of produced water and natural gas volumes associated with oil production from unconventional reservoirs in a time series fashion. The study focuses on the pay zones within the Spraberry and Wolfcamp Formations of the Midland Basin in the U.S. The joint prediction model is based on a deep neural network architecture leveraging long short-term memory (LSTM) layers. Our model has the capability to both reproduce and forecast produced water and natural gas volumes for wells at monthly resolution and has demonstrated 91 percent joint prediction accuracy to held out testing data with little disparity noted in prediction performance between the training and test datasets. Additionally, model predictions replicate water and gas production profiles to wells in the test dataset, even for circumstances that include irregularities in production trends. We apply the model in tandem with an Arps decline model to generate cumulative first and five-year estimates for oil, gas, and water production outlooks at the well and basin-levels. Production outlook totals are influenced by well completion, decline curve, and spatial and reservoir attributes. These types of model-derived outlooks can aid operators in formulating management or remedial solutions for the volumes of fluids expected from unconventional O&G development.