TY - JOUR
T1 - Good wells make better stimulation candidates
T2 - An evidence-based analysis
AU - Jamali, Ali
AU - Ettehadtavakkol, Amin
N1 - Funding Information:
We wish to acknowledge the Bob L. Herd Department of Petroleum Engineering and the Whitacre College of Engineering at Texas Tech University for providing financial support for this research. We also thank Apache Corporation for providing allocated production and injection data for the Slaughter field.
Funding Information:
We wish to acknowledge the Bob L. Herd Department of Petroleum Engineering and the Whitacre College of Engineering at Texas Tech University for providing financial support for this research. We also thank Apache Corporation for providing allocated production and injection data for the Slaughter field.
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/8
Y1 - 2018/8
N2 - Effective candidate selection is an important consideration in planning successful stimulation campaigns. Identifying “high potential” wells—those that would provide the largest incremental production—has been the subject of several studies, some of which have suggested that stimulation of better producers is a good practice to maximize stimulation benefits. An evidence-based investigation of this idea is lacking and is the subject of this study. This paper hypothesizes that a positive correlation exists between a well's oil production performance and its stimulation incremental oil production. We tested this hypothesis by investigating three independent methods: (1) analysis of aggregate results of case studies in the literature, (2) analysis of production and workover data from four mature Permian Basin San Andres leases, and (3) analysis of the simulation results from a tuned reservoir model. The results confirmed the existence and statistical significance of a positive correlation between pre-stimulation oil rate and stimulation incremental oil production. In our field-scale reservoir simulation model, we used pre-stimulation oil rate to rank stimulation candidates, which identified more than 80% of the top candidates. We recommend prioritizing wells that exhibit high oil production for stimulation in order to statistically increase the likelihood of maximized workover benefits.
AB - Effective candidate selection is an important consideration in planning successful stimulation campaigns. Identifying “high potential” wells—those that would provide the largest incremental production—has been the subject of several studies, some of which have suggested that stimulation of better producers is a good practice to maximize stimulation benefits. An evidence-based investigation of this idea is lacking and is the subject of this study. This paper hypothesizes that a positive correlation exists between a well's oil production performance and its stimulation incremental oil production. We tested this hypothesis by investigating three independent methods: (1) analysis of aggregate results of case studies in the literature, (2) analysis of production and workover data from four mature Permian Basin San Andres leases, and (3) analysis of the simulation results from a tuned reservoir model. The results confirmed the existence and statistical significance of a positive correlation between pre-stimulation oil rate and stimulation incremental oil production. In our field-scale reservoir simulation model, we used pre-stimulation oil rate to rank stimulation candidates, which identified more than 80% of the top candidates. We recommend prioritizing wells that exhibit high oil production for stimulation in order to statistically increase the likelihood of maximized workover benefits.
KW - Candidate well selection
KW - Intervention
KW - Permian Basin San Andres
KW - Production enhancement
KW - Stimulation
KW - Workover
UR - http://www.scopus.com/inward/record.url?scp=85045556229&partnerID=8YFLogxK
U2 - 10.1016/j.petrol.2018.03.106
DO - 10.1016/j.petrol.2018.03.106
M3 - Article
AN - SCOPUS:85045556229
SN - 0920-4105
VL - 167
SP - 216
EP - 226
JO - Journal of Petroleum Science and Engineering
JF - Journal of Petroleum Science and Engineering
ER -