Assisted History Matching Using Bayesian Inference: Application to Multi-well Simulation of a Huff-n-Puff Pilot Test in the Permian Basin
- Esmail Eltahan (The University of Texas) | Reza Ganjdanesh (The University of Texas) | Wei Yu (The University of Texas / SimTech LLC) | Kamy Sepehrnoori (The University of Texas) | Hunter Drozd (EP Energy) | Raymond Ambrose (EP Energy)
- Document ID
- Unconventional Resources Technology Conference
- SPE/AAPG/SEG Unconventional Resources Technology Conference, 20-22 July, Virtual
- Publication Date
- Document Type
- Conference Paper
- 2020. Unconventional Resources Technology Conference
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The dynamic nature of unconventional-reservoir developments calls for availability of fast and reliable history matching methods for simulation models. Here, we apply an assisted history-matching (AHM) approach to a pair of wells in the Wolfcamp B and C formations of Midland Basin, for which production history is recorded for two periods: primary production and gas injection (Huff-n-Puff, or HNP ). The recorded history of gas injection reveals severe inter-well interactions, underscoring the importance of fracture interference modeling.
Fracture segments are modeled with embedded discrete fracture model (EDFM). Inter-well communication is modeled using long fractures that only become active during gas injection. We apply a Bayesian AHM algorithm with a neural-network-proxy sampler to quantify uncertainty and find the best model matches. For each well, we use primary production observations to invert for 13 uncertain parameters that describe fracture properties, initial conditions, and relative permeability. Subsequently, by minimizing pressure- and rate-misfit errors during the HNP period, we evaluate the size and conductivity of inter-well fractures. For each AHM study, the objective is to minimize a cost function that is a linear combination of misfit errors between simulation results and observation data for well pressure and production rates of oil, water, and gas. The selected solution samples were used to perform probabilistic forecasts and assess the potential of HNP enhanced oil recovery (EOR) in the area of interest.
From 1400 total simulation runs, the AHM algorithm generated 100 cases (solutions) that satisfy predefined selection criteria. Even though the parameter prior distributions were the same for the two wells, the marginal posteriors were dissimilar. Relative permeability curves for solution candidates can vary significantly from each other. The prospects of EOR were proven decent for the wells of interest. We reported 30% and 81% incremental recovery for the P50 predictions of wells BH and CH, respectively.Introduction
Exploitation activities of tight oil resources (with formation permeability less than 0.1 mD) have been increasing as horizontal drilling and hydraulic fracturing technologies continue to improve. In 2018, 61% of total US crude oil production was produced from tight formations (EIA 2019). A typical tight oil well will be completed over multiple stages creating hundreds of fracture clusters along a horizontal wellbore that extends for thousands of feet. This completion forms a large network of fractures that connects the wellbore to a large surface area of the shale formation. The initial well productivity could be quite high, it typically declines very rapidly and remains low during long term production. Pressure depletion occurs quickly because of the small permeability of tight pores. As a result, recovery factors are only in the 1 to 10% range of the original oil in place during primary production (EIA 2013), leaving significant amounts of unrecovered hydrocarbon in the subsurface.
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