Development of a data-driven forecasting tool for hydraulically fractured, horizontal wells in tight-gas sands

B. Kulga, E. Artun, T. Ertekin

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Tight-gas sand reservoirs are considered to be one of the major unconventional resources. Due to the strong heterogeneity and very low permeability of the formation, and the complexity of well trajectories with multiple hydraulic fractures; there are challenges associated with performance forecasting and optimum exploitation of these resources using conventional modeling approaches. In this study, it is aimed to develop a data-driven forecasting tool for tight-gas sands, which are based on artificial neural networks that can complement the physics-driven modeling approach, namely numerical-simulation models. The tool is designed to predict the horizontal-well performance as a proxy to the numerical model, once the initial conditions, operational parameters, reservoir/hydraulic-fracture characteristics are provided. The data-driven model, that the forecasting tool is based on, is validated with blind cases by estimating the cumulative gas production after 10 years with an average error of 3.2%. A graphical-user-interface application is developed that allows the practicing engineer to use the developed tool in a practical manner by visualizing estimated performance for a given reservoir within a fraction of a second. Practicality of the tool is demonstrated with a case study for the Williams Fork Formation by assessing the performance of various well designs and by incorporating known uncertainties through Monte Carlo simulation. P10, P50 and P90 estimates of the horizontal-well performance are quickly obtained within acceptable accuracy levels.

Original languageEnglish (US)
Pages (from-to)99-110
Number of pages12
JournalComputers and Geosciences
Volume103
DOIs
StatePublished - Jun 1 2017

Fingerprint

Horizontal wells
Sand
well
sand
gas
resource
gas production
artificial neural network
modeling
simulation
Hydraulics
physics
trajectory
permeability
Graphical user interfaces
Numerical models
Physics
Trajectories
Tight gas
Neural networks

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computers in Earth Sciences

Cite this

@article{e09f1d10688c4e7faeaac5e56d364ed7,
title = "Development of a data-driven forecasting tool for hydraulically fractured, horizontal wells in tight-gas sands",
abstract = "Tight-gas sand reservoirs are considered to be one of the major unconventional resources. Due to the strong heterogeneity and very low permeability of the formation, and the complexity of well trajectories with multiple hydraulic fractures; there are challenges associated with performance forecasting and optimum exploitation of these resources using conventional modeling approaches. In this study, it is aimed to develop a data-driven forecasting tool for tight-gas sands, which are based on artificial neural networks that can complement the physics-driven modeling approach, namely numerical-simulation models. The tool is designed to predict the horizontal-well performance as a proxy to the numerical model, once the initial conditions, operational parameters, reservoir/hydraulic-fracture characteristics are provided. The data-driven model, that the forecasting tool is based on, is validated with blind cases by estimating the cumulative gas production after 10 years with an average error of 3.2{\%}. A graphical-user-interface application is developed that allows the practicing engineer to use the developed tool in a practical manner by visualizing estimated performance for a given reservoir within a fraction of a second. Practicality of the tool is demonstrated with a case study for the Williams Fork Formation by assessing the performance of various well designs and by incorporating known uncertainties through Monte Carlo simulation. P10, P50 and P90 estimates of the horizontal-well performance are quickly obtained within acceptable accuracy levels.",
author = "B. Kulga and E. Artun and T. Ertekin",
year = "2017",
month = "6",
day = "1",
doi = "10.1016/j.cageo.2017.03.009",
language = "English (US)",
volume = "103",
pages = "99--110",
journal = "Computers and Geosciences",
issn = "0098-3004",
publisher = "Elsevier Limited",

}

Development of a data-driven forecasting tool for hydraulically fractured, horizontal wells in tight-gas sands. / Kulga, B.; Artun, E.; Ertekin, T.

In: Computers and Geosciences, Vol. 103, 01.06.2017, p. 99-110.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Development of a data-driven forecasting tool for hydraulically fractured, horizontal wells in tight-gas sands

AU - Kulga, B.

AU - Artun, E.

AU - Ertekin, T.

PY - 2017/6/1

Y1 - 2017/6/1

N2 - Tight-gas sand reservoirs are considered to be one of the major unconventional resources. Due to the strong heterogeneity and very low permeability of the formation, and the complexity of well trajectories with multiple hydraulic fractures; there are challenges associated with performance forecasting and optimum exploitation of these resources using conventional modeling approaches. In this study, it is aimed to develop a data-driven forecasting tool for tight-gas sands, which are based on artificial neural networks that can complement the physics-driven modeling approach, namely numerical-simulation models. The tool is designed to predict the horizontal-well performance as a proxy to the numerical model, once the initial conditions, operational parameters, reservoir/hydraulic-fracture characteristics are provided. The data-driven model, that the forecasting tool is based on, is validated with blind cases by estimating the cumulative gas production after 10 years with an average error of 3.2%. A graphical-user-interface application is developed that allows the practicing engineer to use the developed tool in a practical manner by visualizing estimated performance for a given reservoir within a fraction of a second. Practicality of the tool is demonstrated with a case study for the Williams Fork Formation by assessing the performance of various well designs and by incorporating known uncertainties through Monte Carlo simulation. P10, P50 and P90 estimates of the horizontal-well performance are quickly obtained within acceptable accuracy levels.

AB - Tight-gas sand reservoirs are considered to be one of the major unconventional resources. Due to the strong heterogeneity and very low permeability of the formation, and the complexity of well trajectories with multiple hydraulic fractures; there are challenges associated with performance forecasting and optimum exploitation of these resources using conventional modeling approaches. In this study, it is aimed to develop a data-driven forecasting tool for tight-gas sands, which are based on artificial neural networks that can complement the physics-driven modeling approach, namely numerical-simulation models. The tool is designed to predict the horizontal-well performance as a proxy to the numerical model, once the initial conditions, operational parameters, reservoir/hydraulic-fracture characteristics are provided. The data-driven model, that the forecasting tool is based on, is validated with blind cases by estimating the cumulative gas production after 10 years with an average error of 3.2%. A graphical-user-interface application is developed that allows the practicing engineer to use the developed tool in a practical manner by visualizing estimated performance for a given reservoir within a fraction of a second. Practicality of the tool is demonstrated with a case study for the Williams Fork Formation by assessing the performance of various well designs and by incorporating known uncertainties through Monte Carlo simulation. P10, P50 and P90 estimates of the horizontal-well performance are quickly obtained within acceptable accuracy levels.

UR - http://www.scopus.com/inward/record.url?scp=85015321022&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85015321022&partnerID=8YFLogxK

U2 - 10.1016/j.cageo.2017.03.009

DO - 10.1016/j.cageo.2017.03.009

M3 - Article

AN - SCOPUS:85015321022

VL - 103

SP - 99

EP - 110

JO - Computers and Geosciences

JF - Computers and Geosciences

SN - 0098-3004

ER -