Comparison of Numerical vs. Analytical Models for EUR Calculation & Optimization in Unconventional Reservoirs
During this webinar, Dr. Jim Erdle used several case studies to:
- Quantify differences in EUR predicted by analytical models and numerical simulation
- Reveal underestimation of EUR (up to 60%) when using RTA with analytical models for history matching
- Identify an efficient numerical simulation workflow for probabilistic forecasting of brown fields
- Show why Data Analytics produces poor Predictive Models for unconventional reservoirs when physics-based predictors (i.e. permeability, porosity, etc.) are ignored
AbstractAnalytical models available in Rate-Transient-Analysis (RTA) packages are widely used as fast tools for history matching and forecast in unconventional resources. In addition, recently, there has been an increasing interest in numerical simulation of unconventional reservoirs. In this study, we use both methods to history match fractured unconventional wells, followed by forecast calculations. This study aims to reveal large differences in Estimated Ultimate Recovery (EUR), predicted by analytical models and numerical simulation in unconventional reservoirs.
First, we consider a single-phase shale oil reservoir as a base case for this study. The base case also satisfies other assumptions inherent in analytical models such as homogenous reservoir properties and fully-penetrating planar fractures with constant half-length and conductivity. An excellent match between results of two methods for the base model validates the simulation approach. We then impose different real-world deviations from RTA assumptions and investigate reliability of EUR predictions made by both approaches. We also examine dry gas and gas condensate shale reservoirs. In all cases, historical data and reference EURs are derived from fine-grid simulations.
Example results show that, in the presence of real-world deviations from RTA assumptions, analytical models can still match the historical production data; however, key reservoir and fracture parameters need to be modified drastically to compensate lack of sufficient physics in analytical models. Results show that these history-matched models are not predictive for future production, providing highly pessimistic EURs in most real-world scenarios. For the cases presented in this study, analytical models under-predicts EURs by 10-20% although history match of two-year production looks good. This error in EUR increases drastically (up to 50-60%) as the length of historical data decreases from 2 years to 3 months.
For all cases, we also apply an efficient simulation workflow for probabilistic forecasting of brown fields. This workflow provides multiple history-matched models that are constrained by historical production data. The probabilistic forecast provides P90 (conservative), P50 (most likely), and P10 (optimistic) values for EUR. In all examples, range of P90 to P10 values includes the reference EUR and the P50 values are within 7% error of the reference EUR.
Dr. Jim Erdle has 40 years of industry experience, primarily in reservoir and production engineering-related positions within the services and software segments of the E&P industry, since graduating from Penn State with BS and PhD degrees in Petroleum Engineering in ’71 and ’74. Early in his career, Jim was involved with some of the industry’s leading advances in well testing design, monitoring and interpretation technology, including Closed Chamber and Surface Pressure Readout (SPRO) Drill Stem Testing, production enhancement via NODAL analysis, stimulation treatment design & monitoring techniques, and production surveillance software (The Production Analyst, which was the predecessor to OFM).
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