The gas/oil ratio (GOR) of unconventional wells in the Midland Basin is becoming an increasingly important topic, due to both transportation capacity and the general price conditions for gas products. Coupled with this, interwell spacing tests have caused many to rethink the economic viability of certain parts of shale basins. This study presents a machine learning approach to predicting GOR in the Midland Basin over the first 2 years of a well’s production history.
We utilize a dataset compiled from public sources to make predictions of oil, gas, and water production for all horizontal unconventional wells in the Midland basin. The prediction outputs are a time series at 30 day increments, so that we can observe the decline rates of each product with time. This multi-input-multi-output (MIMO) model utilizes completions information, interwell spacing, parent child calculations, and geology information sourced from well logs.
Using the results of the model, we generate Shapley values, a method of interrogation which model features (interwell spacing, proppant loading, porosity, etc) had the greatest impact on the predicted production profiles. This method isolates the impacts of geological factors from stimulation intensity, and in turn, interwell spacing.
Number of Pages
Looking for more?
Some of the OnePetro partner societies have developed subject- specific wikis that may help.