No abstract

The quality and quantity of information available in the public domain is growing rapidly. Companies are creating in-house databases to track and improve operating and service performances in an effort to keep up with this explosion of data. The hardware and software used to obtain and manipulate massive amounts of information are constantly improving. All these events have created an opportunity to evaluate the complex interaction of variables and quantify how they relate to the required end result. The Redfork formation is a prolific, low-permeability, natural gas and gas-condensate reservoir deposited during the middle Pennsylvanian Period. The reservoir is located in the deep Anadarko basin of west-central Oklahoma. The Redfork is an interesting reservoir because of its high level of heterogeneity and the varied stimulation/completion methods used in the formation. The volume and diversity of information available on Redfork completions make the Redfork formation unique. This paper analyzes Redfork completions in Roger Mills and Custer Counties. The study uses artificial neural networks (ANNs) trained on a data set of 107 Redfork completions to analyze and quantify the effect of well/reservoir parameters and completion methods on production results. Specific areas of interest include controllable/quantifiable aspects of a well's completion/stimulation procedure that affect the production outcome. The paper will document a methodological test that supplements standard completion-optimization techniques. P. 555

@yri9ht199S, %ciaty c!/ Pabolaum Enghaara, k. This papnr was prepared fcf pfeaenlatii at IfM 1SSS SPE Rc&y Mountain RagionaVLcw-Psm'mability Reservoirs Symposium and WWficm bald m Dsnver, Colwado, S-S April 1SS6. Tlvs pa@r was salected for presmtation by an SPE Program CommMee following review d Wmnation contained h an abatnxt 5Mwnitted W the SS.Ithcf[s). Cc@ank d the paper, as p_asanted, have cat ken reviawed by the Scc@y d Pehlaum Enginaam and we subject to axaclkm EW tha auihcf(s) Tha material. as presOrW-d, dcas not necassarity refkt any position c4 ttw Sociaty d Patrolewn Engmeere, its ofkara, or membars. Pepars presented at SPE maatings am aubjad 10 pubkation raviaw by Editorial CommiKaas of the Society of PetfOieum Engineers. Ektrlmii Npc&dOn , distribution, or storage M any part of this papar for mrnmercial purposes W&X4 the writtan cunsanl C4 tha Sockty of Patrolman Engineers is pohibiwd Pannission to rafxduca in print m mstrktad to an abstracl d not mom than 300 wwds; itiuslrat"mns may rid be cc@3d. Tha abstract must contain campicuous dmwbdgment cf whera and by w4wxn the paper was presented. Write LtiariarI, SPE, P.O. s0xss3a2e.R~, TX ?S3S3-3S3S, U.S.A. fax 01-972-9S2-943S. AbstractThe quality and quantity of information available in the public domain is growing rapidly. Companies are creating in-house databases to track and improve operating and service performances in an effort to keep up with this explosion of data. The hardware and software used to obtain and manipulate massive amounts of information are constantly improving. All these events have created an opportunity to evaluate the compIex interaction of variables and quanti~how they relate to the required end result.The Redfork formation is a prolific, low-permeability, natural gas and gas-condensate reservoir deposited during the middle PennsylvanianPeriod. The reservoir is located in the deep Anadarko basin of west-central C)klahoma. The Redfork is an interesting reservoir because of its high level of heterogeneity and the varied stimulation/completion methods used in the formation. The volume and diversity of information available on Redfork completions make the Redfork formation unique.This paper analyzes Redfork completions in Roger Mills and Custer Counties. The study uses artificial neural networks (ANNs) trained on a data set of 10'7 Redfork completions to analyze and quanti~the effect of well/reservoir parameters and completion methods on production results. Specific areas of interest include controllable/quant ifiable aspects of a well's completiotistimulation procedure that affect the production outcome. The paper will document a methodological test that supplements standard completion-optimization techniques. ANNs