Impact of Geological Variables in Controlling Oil-Reservoir Performance: An Insight from a Machine-Learning Technique

Abstract

Summary Predicting oilfield performance is extremely challenging because of the large number of variables that can influence and control it. Traditional methods such as decline-curve analysis have been commonly used but have been shown to have significant shortcomings. In recent years, advances in machine learning (ML) have provided a new suite of tools to tackle complex multivariant problems such as understanding oil-reservoir performance and predicating the final recovery factor. In this study, the application of a random-forest algorithm to train three predictive models and investigate the influence of the various input variables was investigated. To train the algorithm, a database was built that includes information on 32 variables from 93 reservoirs from the Norwegian Continental Shelf. These variables control or potentially influence field performance and include factors that are a function of geology, subsurface conditions, fluids, and the engineering decisions taken in field development. In addition to these controlling parameters, data were also recorded for the fields that record performance. These included information on the estimated recovery factor and production rates. Eighty percent of the data were input into the random-forest algorithm to train the models, whereas 20% were retained to blind test the subsequent models. Model accuracy was measured by comparing actual and predicted observations for each prediction metric using an R2 score, mean square error, and root mean square error. The production-rate model had a mean square error of 0.004, whereas the mean square error for recovery factor was 0.024. Estimates of average monthly depletion rate have a mean square error of 0.0104. Predictor importance estimates indicate that geology/depth-dependent variables such as stratigraphic heterogeneity, reservoir depth of burial, average porosity, and diagenetic impact are among the variables with high importance in predicting recovery factor. When predicting reservoir-oil rate, the most important variables are related to field size, such as cumulative oil produced, number of wells, oil in place (OIP), and bulk rock volume. In this study, we provide data-driven insight into understanding the relationship between subsurface and engineering conditions of reservoir producibility; we also provide a tool for predicating reservoir performance within a basin or region.

Sensitivity of Erosion‐Rate in Permafrost Landscapes to Changing Climatic and Environmental Conditions Based on Lake Sediments From Northwestern Alaska

“…The predictive power of the trained trees can then be evaluated based on the observations that were not used in the training (i.e., out of bag observations). Random forest models are advantageous in that they do not make assumptions about data distribution and the form of the relations between the predictors and response variable and their prediction is mostly insensitive to correlated predictors (e.g., Aliyuda et al., 2020; Konapala & Mishra, 2020; Vaughan et al., 2017). Random forest models are particularly suitable for analysis of complex natural systems that are influenced by multiple factors (e.g., Aliyuda et al., 2020; Cotton et al., 2016; Konapala & Mishra, 2020; Rafat et al., 2021; Vaughan et al., 2017) (a more detailed description of random forest is available in Supporting Information S1).…”

Towards more accurate and explainable supervised learning-based prediction of deliverability for underground natural gas storage

“…Several studies have examined the relationship between geological/engineering factors with reservoir performance metrics such as reservoir daily production rate, depletion rate, and recovery factor. The authors in [39] and [40] reported a strong relationship between reservoir performance and reservoir volumes as well as size of the field or reservoir. Similarly, [41] suggested that working gas, base gas, and total gas are the key factors that control the deliverability of underground natural gas storage.…”