Modern Decline Curve Analysis of Unconventional Reservoirs: A Comparative Study Using Actual Data

  • Wahba A
  • Khattab H
  • Tantawy M
  • et al.
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Abstract

Petroleum consumption increases around the world and production of conventional reservoirs can’t cover the increased demand. So, producing unconventional resources is an imperative necessity. Unconventional resources are characterized by very low permeability. Drilling horizontal wells in these resources and completed them with multiple hydraulic fractures make the reservoir. Hydraulic fractures work as paths for hydrocarbon to flow toward the wellbore to achieve an economic production rate. Production behaviour of these wells is characterized by long-term transient flow followed by boundary-dominated flow. Many decline curve analysis models have been developed to simulate this behaviour, but none of them can capture all flow-regime types. This paper reviewed the most popular and used decline curve analysis models: Arps model, power-law exponential model, stretched exponential production decline model, T-model, logistic growth model, Duong model, Yu-Miocevic model and extended exponential decline curve. This paper summarized the origins, derivations and assumptions of these eight models. This paper also presents a comparative study of these models using production data from unconventional gas and oil reservoirs. To facilitate conducting this study, the eight decline curve analysis models were programmed in a software application written in python language. This software application calibrated models’ parameters to production data using trust region reflective algorithm. The value of estimated ultimate recovery predicted using this software application is consistent with that predicted using the linear flow analysis model. The comparative study can serve as a guideline for petroleum engineers to determine when to use each model.

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APA

Wahba, A., Khattab, H., Tantawy, M., & Gawish, A. (2022). Modern Decline Curve Analysis of Unconventional Reservoirs: A Comparative Study Using Actual Data. Journal of Petroleum and Mining Engineering, 0(0), 0–0. https://doi.org/10.21608/jpme.2022.128147.1123

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