Abstract
The objective of this work is to evaluate the efficacy of empirical models in forecasting oil production in shale reservoirs, bycomparing and analyzing their fit and effectiveness to our dataset. The following three modelswere considered: A Conventional Decline Curve Analysis (CDC), an Unconventional Rate Decline (URD) Approach, and a Logistics Growth Analysis (LGA) method. A comparative study is performed to evaluate the use of Artificial Neural Networks (ANN) for production forecasts and to reinforce the thinking that it is imperative to include physical parameters in mathematical models to predict accurateforecasts.
For this project, we used non-linear regression to fit empirical models to the dataset obtained from North Dakota Industrial Commission (NDIC). We evaluated the fit of modelswith the help of coefficient of determination. Physical parameters, such as porosity, saturation, shale volume, etc., and log data from sonic logs, gamma ray logs, etc., were selected as input to the ANN model andwere aided by Analysis of Variances (ANOVA).
Amongst the empirical models for shale play, URD method is the most commonly used since it is idealfor fractured reservoirs with extremely low permeability. URD model did fit the cumulative production profiles, but could not accurately fit the monthly production profile. The CRD approach was overallunsuccessful in generating accurate future production profiles. Values forecasted from the ANN show less than 10% error in estimation. The inclusion of physical parameters has proven to be extremely promising in the forecast production from fields that do not have sufficient history for statistical fitting.
Through aselection of physical properties from different sources, we have built an ANN model that fits with the production data in wells that have adiverse production history. Our work has shown the importance of including physical parameters into a process that was heretofore seen as a time series regression problem. In general, our new ANN-based method generated the best results.
