Saeed Zargari

Modeling, History Matching, Forecasting and Analysis of Shale Reservoirs performance Using Artificial Intelligence

Producing hydrocarbon from Shale plays has attracted much attention in the recent years. Advances in horizontal drilling and multi-stage hydraulic fracturing have made shale reservoirs a focal point for many operators. Our understanding of the complexity of the flow mechanism in the natural fracture and its coupling with the matrix and the induced fracture, impact of geomechanical parameters and optimum design of hydraulic fractures has not necessarily kept up with our interest in these prolific and hydrocarbon rich formations. In this paper we discuss using a new and completely different approach to modeling, history matching, forecasting and analyzing oil and gas production in shale reservoirs. In this new approach instead of imposing our understanding of the flow mechanism and the production process on the reservoir model, we allow the production history, well log, and hydraulic fracturing data to force their will on our model and determine its behavior. In other words, by carefully listening to the data from wells and the reservoir we developed a data driven model and history match the production process from Shale reservoirs. The history matched model is used to forecast future production from the field and to assist in planning field development strategies. We use the last several months of production history as blind data to validate the model that is developed. This is a unique and innovative use of pattern recognition capabilities of artificial intelligence and data mining as a workflow to build a full field reservoir model for forecasting and analysis of oil and gas production from shale formations. Examples of three case studies in Lower Huron and New Albany shale formations (gas producing) and Bakken Shale (oil producing) is presented in this paper.

Top-down, intelligent reservoir modelling of oil and gas producing shale reservoirs: case studies

Producing hydrocarbon from shale plays has attracted much attention in recent years. Advances in horizontal drilling and multi-stage hydraulic fracturing have made shale reservoirs a focal point for many operators. Our understanding of the complexities associated with the flow mechanism in shale has not kept up with our interest in shale formations. We present the application of a new reservoir modelling approach to history matching, forecasting and predicting hydrocarbon production from shale reservoirs, where instead of imposing our understanding on the reservoir model, we allow the production history, well log, and hydraulic fracturing data to force their will on our model. By carefully listening to the data, we developed a data-driven model and history match the production process and validate our model (using blind production history). Examples of three case studies in Lower Huron and New Albany shale formations (gas producing) and Bakken shale (oil producing) are presented in this article.