Engineering Poster E7: A new approach for production forecasting from individual layers in multi-layer commingled tight gas reservoirs
Katarina Van Der Haar (nee Kosten) A *A University of Adelaide, Australian School of Petroleum and Energy Resources (ASPER), Adelaide SA, Australia.
The APPEA Journal 62 - https://doi.org/10.1071/AJ21419
Published: 3 June 2022
Abstract
Poster E7
Estimating future production in multi-layer tight gas reservoirs is necessary, but problematic. Initial production looks promising but soon decreases to levels that are much lower than first anticipated. Boundary-dominated flow to which common production data analysis can be applied to, takes up to 8 years to establish, yet future production estimations must be made at the time of drilling. Common industry software does not fully assess individual layer contribution because large uncertainties exist in layer-specific contributions to total production. Many engineers tend to use analysis methods designed for single-layer wells, which leaves future production estimations with large errors, and this can have potentially long ranging consequences for a company. This paper presents a new way of estimating future production in multi-layer tight gas reservoirs by incorporating the uncertainty that can be found in individual layer contributions. Our approach utilises single tank material balance and matches total well production data by changing layer-specific properties. A workflow has been created for the programming language Python that includes a Bayesian element to honour the uncertainty in individual layer effective permeability and individual layer gas in place. Applying this workflow to many wells in a specified area allows probability distribution functions for layer effective permeability and layer gas in place to be generated. This will result in attaining more realistic production forecasts in the form of P10, P50 and P90 for multi-layer tight gas reservoirs, and allowing the engineer to make better-informed early assessments of the future production of wells.
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Keywords: Bayesian inference, multi-layer flow, multi-layered tight gas reservoirs, production forecasting, production prediction, Python, reserves estimation, uncertainty, uncertainty quantification, unconventional reservoirs.
Katarina Van Der Haar (nee Kosten) holds a Bachelor of Petroleum Engineering and Bachelor of Science (Geology and Geophysics) with First Class Honours from the University of Adelaide. She also holds a Bachelor of International Studies (Arabic and International Relations) with distinction from Deakin University. Her interest in the oil and gas industry was sparked while working on rigs as a roughneck for AJ Lucas, Interdrill, Australian Drilling Services and Saxon Energy Services (now SLB Landrigs) in the Cooper, Surat and Canning basins. She has previously worked for Santos focusing on underbalanced drilling and on production prediction in multi-layer tight gas reservoirs. She has most recently worked on the Gorgon Gas Supply with Chevron. She volunteered on local and federal chapters of several industry committees including SPE, PESA and ASEG. She is currently the Co-Chair for Marketing & Promotions for the 2022 SPE Asia Pacific Oil & Gas Conference & Exhibition and is on the Board of Governors for the Energy Club WA. This is the work of her Honours Thesis. |
