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A Case History: Evaluating Well Completions in Eagle Ford Shale Using a Data-Driven Approach (SPE 173336)

3-5 February 2015

Unconventional shale resources are key hydrocarbon sources, gaining importance and popularity as hydrocarbon reservoirs both within the United States and internationally. Horizontal wells and multiple transverse hydraulic fracturing are instrumental factors for economical production from shale assets. Hydraulic fracturing typically represents a major component of total well completion costs, and many efforts have been made to study and investigate different strategies to improve well production and to reduce costs. The focus of this paper is completion evaluation in different parts of the Eagle Ford shale and our objective is to identify appropriate completion strategies in throughout field.

A data-driven neural network model is trained on the database comprised of multiple operators’ well data. In this model, mud logs are used as indicators for geology and reservoir related parameters such as pressure, fluid saturation and permeability. Additionally, completion and fracture related parameters are also used as model inputs. Because wells are pressure managed differently, normalized oil and gas production used as model outputs. Thousands of neural networks are trained using genetic algorithm in order to fully exploit hidden correlations within the database. This results in selection of a neural network that is able to understand differences between different wells and recommend on improving completion/stimulation designs.

Final neural network model is successfully tested on two separate datasets located on different parts of the Eagle Ford shale oil window. Further, additional test dataset comprised of eight wells is used to validate the proposed data-driven model. Fracture designs of test wells include seven ceramic wells and one natural sand well. Accurate predictions demonstrate the utility of the Eagle Ford model in assessing well completion effectiveness. Under producing wells were also identified by the model and new fracture designs recommended to improve well productivity.

This paper will be useful for understanding the effects of completion and fracture treatment designs on well productivity in the Eagle Ford shale. This information will help operators select more effective treatment designs which can reduce operational costs associated with completion/fracturing and can improve oil and gas production.

Author(s): Amir Mohammad Nejad: StrataGen Inc., Stanislav Sheludko: StrataGen Inc., Robert Frank Shelley: StrataGen Inc., Trey Hodgson: Sundance Energy, Patrick Riley Mcfall: Sundance Energy

Paper Number: SPE-173336-MS

URL: https://www.onepetro.org/conference-paper/SPE-173336-MS


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