Open Access
Xi, Zhenke
Graduate Program:
Petroleum and Mineral Engineering
Master of Science
Document Type:
Master Thesis
Date of Defense:
February 12, 2019
Committee Members:
  • Eugene C Morgan, Thesis Advisor
  • Derek Elsworth, Committee Member
  • John Yilin Wang, Committee Member
  • Eugene C Morgan, Committee Member
  • unconventional shale
  • data analytics
  • gas production forecast
  • decline curve analysis
  • geostatistics
Traditionally, in order to estimate the production potential at a new, prospective field site via simulation or material balance, one needs to collect various forms of expensive field data and/or make assumptions about the nature of the formation at that site. Decline curve analysis would not be applicable in this scenario, as producing wells need to pre-exist in the target field. The objective of our work is to make first-order forecasts of production rates at prospective, undrilled sites using only production data from existing wells in the entire play. This is accomplished through co-kriging of decline curve parameter values, where the parameter values are obtained at each existing well by fitting an appropriate decline model to the production history. Co-kriging gives the best linear unbiased prediction of parameter values at undrilled locations, and also estimates uncertainty in those predictions. Thus, we can obtain production forecasts at P10, P50, and P90, as well as calculate EUR at those same levels, across the spatial domain of the play. To demonstrate the proposed methodology, we use monthly gas flow rates and well locations from the Marcellus shale gas play in this research. Looking only at horizontal and directional wells, the gas production rates at each well are carefully filtered and screened. Also, we normalize the rates by perforation interval length. We keep only production histories of 24 months or longer in duration to ensure good decline curve fits. Ultimately, we are left with 5,637 production records. Here, we choose Duong’s decline model to represent production decline in this shale gas play, and fitting of this decline curve is accomplished through ordinary least square regression. Interpolation is done by universal co-kriging with consideration to correlation between the four parameters in Duong’s model, which also show linear trends (the parameters show dependency on the x and y spatial coordinates). Kriging gives us the optimal decline curve coefficients at new locations (P50 curve), as well as the variance in these coefficient estimates (used to establish P10 and P90 curves). We are also able to map EUR for 25 years across the study area. Finally, the universal co-kriging model is cross-validated with a leave-one-out scheme, which shows significant but not unreasonable error in decline curve coefficient prediction. The methods proposed are easy to implement and do not require various expensive data like permeability, bottom hole pressure, etc., giving operators a risk-based analysis of prospective sites. While we demonstrate the procedure on the Marcellus shale gas play, it is applicable to any play with existing producing wells. We also make this analysis available to the public in a user-friendly web app.