This paper discusses how machine learning by use of multiple linear regression and a neural network was used to optimize completions and well designs in the Duvernay shale. The methodology revealed solutions that could save more than $1 million per well and potentially deliver an improvement in well performance of greater than 50%. The work flow described rigorously analyzes the relationships between a significant number of well-completion variables, predicts results, and performs optimizations for ideal outcomes. The work flow is not Duvernay-specific and can be applied to other basins and formations.
A fundamental problem for machine learning in many industries is that a responding variable is controlled not by one but by a number of predictor variables. Inferring the relationship between the responding variable and the predictor variables is of key importance. Interactions between predictor variables and noise in the data complicate matters further. This problem can be solved with multiple linear regression or a neural network, both of which use all of the predictor variables together. However, care must be taken to obtain a model that is truly predictive and not merely a result of overfitting the data.
In unconventional oil and gas reservoirs, well performance generally is characterized either at the well level by detailed technical work such as rate-transient analysis, microseismic, and other techniques or at the field level by statistical methods with ranges for production performance. Refinement of this statistical interpretation generally involves normalizing for only one or two key parameters, such as lateral length or tonnage. Additionally, wells usually are grouped or excluded entirely from the population for various reasons, such as substandard completion design. This introduces bias in the selected wells and reduces the sample size. As a result, this approach is limited to the key variables identified and the bias introduced by the well population selected.
The idea of using a neural network has been executed successfully in the past to optimize completions. However, data sets were limited. Recently, the use of machine learning has grown substantially by integrating more variables in the analysis, which reduces reservoir uncertainty.