Integration of seismic attributes and production data for infill drilling strategies - A virtual intelligence approach

P. Thararoop, Zuleima Karpyn, A. Gitman, Turgay Ertekin

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Field development strategies are at the forefront of common engineering practices in the oil and gas industry. Reservoir simulation is the most commonly applied methodology to generate an optimum field development plan. However, reservoir simulation can be an energy and cost intensive method that often relies on rather subjective assumption of input parameters, due to lack of accurate field data. In this paper, a new approach using Artificial Neural Network (ANN) technology is proposed to predict individual well performances and accordingly develop infill drilling strategies. Due to its predictive capabilities, ANN is used as a tool to construct a correlation for production prediction. Seismic attributes, which capture heterogeneity of the reservoir geology, and completion information are used as network inputs. In calculating the interference effects, the geometry of the flow system under consideration was used together with the geometric location and the starting production schedule of each well within the system. The method was successfully implemented on a case study of the 19N 94W Township of the Wamsutter field in Wyoming using actual seismic attributes, completion information, well configuration, and production data. Production predictions were generated by the network for all locations at which seismic attributes were available. More promising locations were then selected for infill drilling purposes based on predicted productions at these locations. The predicted initial rate and 10-yr cumulative production were considered in the selection of infill drilling locations with high productivity potential. Results from this work show that the ANN was able to map the relationship between production, completion information, interference effects, and reservoir characteristics captured in seismic attributes. The proposed methodology allowed the construction of spatial maps of gas production, revealing new sweet spots which could not be identified from the existing production history alone. The production maps derived from the ANN predictions contain important heterogeneous features associated with reservoir properties reflected in seismic data. Even though well interference was initially thought to have a limited effect on well performance for the case study presented, the incorporation of well interference parameters in the network design improved production predictions, suggesting that well interference has a more significant impact on well performance than originally anticipated.

Original languageEnglish (US)
Pages (from-to)43-52
Number of pages10
JournalJournal of Petroleum Science and Engineering
Volume63
Issue number1-4
DOIs
StatePublished - Dec 1 2008

Fingerprint

Infill drilling
infill
artificial neural network
drilling
prediction
Neural networks
network design
methodology
gas industry
oil industry
development strategy
gas production
simulation
seismic data
geology
attribute
geometry
engineering
productivity
history

All Science Journal Classification (ASJC) codes

  • Fuel Technology
  • Geotechnical Engineering and Engineering Geology

Cite this

@article{b0981afdadf74a10bdd3ac4475a791cc,
title = "Integration of seismic attributes and production data for infill drilling strategies - A virtual intelligence approach",
abstract = "Field development strategies are at the forefront of common engineering practices in the oil and gas industry. Reservoir simulation is the most commonly applied methodology to generate an optimum field development plan. However, reservoir simulation can be an energy and cost intensive method that often relies on rather subjective assumption of input parameters, due to lack of accurate field data. In this paper, a new approach using Artificial Neural Network (ANN) technology is proposed to predict individual well performances and accordingly develop infill drilling strategies. Due to its predictive capabilities, ANN is used as a tool to construct a correlation for production prediction. Seismic attributes, which capture heterogeneity of the reservoir geology, and completion information are used as network inputs. In calculating the interference effects, the geometry of the flow system under consideration was used together with the geometric location and the starting production schedule of each well within the system. The method was successfully implemented on a case study of the 19N 94W Township of the Wamsutter field in Wyoming using actual seismic attributes, completion information, well configuration, and production data. Production predictions were generated by the network for all locations at which seismic attributes were available. More promising locations were then selected for infill drilling purposes based on predicted productions at these locations. The predicted initial rate and 10-yr cumulative production were considered in the selection of infill drilling locations with high productivity potential. Results from this work show that the ANN was able to map the relationship between production, completion information, interference effects, and reservoir characteristics captured in seismic attributes. The proposed methodology allowed the construction of spatial maps of gas production, revealing new sweet spots which could not be identified from the existing production history alone. The production maps derived from the ANN predictions contain important heterogeneous features associated with reservoir properties reflected in seismic data. Even though well interference was initially thought to have a limited effect on well performance for the case study presented, the incorporation of well interference parameters in the network design improved production predictions, suggesting that well interference has a more significant impact on well performance than originally anticipated.",
author = "P. Thararoop and Zuleima Karpyn and A. Gitman and Turgay Ertekin",
year = "2008",
month = "12",
day = "1",
doi = "10.1016/j.petrol.2008.08.002",
language = "English (US)",
volume = "63",
pages = "43--52",
journal = "Journal of Petroleum Science and Engineering",
issn = "0920-4105",
publisher = "Elsevier",
number = "1-4",

}

Integration of seismic attributes and production data for infill drilling strategies - A virtual intelligence approach. / Thararoop, P.; Karpyn, Zuleima; Gitman, A.; Ertekin, Turgay.

In: Journal of Petroleum Science and Engineering, Vol. 63, No. 1-4, 01.12.2008, p. 43-52.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Integration of seismic attributes and production data for infill drilling strategies - A virtual intelligence approach

AU - Thararoop, P.

AU - Karpyn, Zuleima

AU - Gitman, A.

AU - Ertekin, Turgay

PY - 2008/12/1

Y1 - 2008/12/1

N2 - Field development strategies are at the forefront of common engineering practices in the oil and gas industry. Reservoir simulation is the most commonly applied methodology to generate an optimum field development plan. However, reservoir simulation can be an energy and cost intensive method that often relies on rather subjective assumption of input parameters, due to lack of accurate field data. In this paper, a new approach using Artificial Neural Network (ANN) technology is proposed to predict individual well performances and accordingly develop infill drilling strategies. Due to its predictive capabilities, ANN is used as a tool to construct a correlation for production prediction. Seismic attributes, which capture heterogeneity of the reservoir geology, and completion information are used as network inputs. In calculating the interference effects, the geometry of the flow system under consideration was used together with the geometric location and the starting production schedule of each well within the system. The method was successfully implemented on a case study of the 19N 94W Township of the Wamsutter field in Wyoming using actual seismic attributes, completion information, well configuration, and production data. Production predictions were generated by the network for all locations at which seismic attributes were available. More promising locations were then selected for infill drilling purposes based on predicted productions at these locations. The predicted initial rate and 10-yr cumulative production were considered in the selection of infill drilling locations with high productivity potential. Results from this work show that the ANN was able to map the relationship between production, completion information, interference effects, and reservoir characteristics captured in seismic attributes. The proposed methodology allowed the construction of spatial maps of gas production, revealing new sweet spots which could not be identified from the existing production history alone. The production maps derived from the ANN predictions contain important heterogeneous features associated with reservoir properties reflected in seismic data. Even though well interference was initially thought to have a limited effect on well performance for the case study presented, the incorporation of well interference parameters in the network design improved production predictions, suggesting that well interference has a more significant impact on well performance than originally anticipated.

AB - Field development strategies are at the forefront of common engineering practices in the oil and gas industry. Reservoir simulation is the most commonly applied methodology to generate an optimum field development plan. However, reservoir simulation can be an energy and cost intensive method that often relies on rather subjective assumption of input parameters, due to lack of accurate field data. In this paper, a new approach using Artificial Neural Network (ANN) technology is proposed to predict individual well performances and accordingly develop infill drilling strategies. Due to its predictive capabilities, ANN is used as a tool to construct a correlation for production prediction. Seismic attributes, which capture heterogeneity of the reservoir geology, and completion information are used as network inputs. In calculating the interference effects, the geometry of the flow system under consideration was used together with the geometric location and the starting production schedule of each well within the system. The method was successfully implemented on a case study of the 19N 94W Township of the Wamsutter field in Wyoming using actual seismic attributes, completion information, well configuration, and production data. Production predictions were generated by the network for all locations at which seismic attributes were available. More promising locations were then selected for infill drilling purposes based on predicted productions at these locations. The predicted initial rate and 10-yr cumulative production were considered in the selection of infill drilling locations with high productivity potential. Results from this work show that the ANN was able to map the relationship between production, completion information, interference effects, and reservoir characteristics captured in seismic attributes. The proposed methodology allowed the construction of spatial maps of gas production, revealing new sweet spots which could not be identified from the existing production history alone. The production maps derived from the ANN predictions contain important heterogeneous features associated with reservoir properties reflected in seismic data. Even though well interference was initially thought to have a limited effect on well performance for the case study presented, the incorporation of well interference parameters in the network design improved production predictions, suggesting that well interference has a more significant impact on well performance than originally anticipated.

UR - http://www.scopus.com/inward/record.url?scp=57049110413&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=57049110413&partnerID=8YFLogxK

U2 - 10.1016/j.petrol.2008.08.002

DO - 10.1016/j.petrol.2008.08.002

M3 - Article

AN - SCOPUS:57049110413

VL - 63

SP - 43

EP - 52

JO - Journal of Petroleum Science and Engineering

JF - Journal of Petroleum Science and Engineering

SN - 0920-4105

IS - 1-4

ER -