No abstract

Low primary recovery percentages from unconventional reservoirs have long motivated interest in Enhanced Oil Recovery (EOR) for these reservoirs, resulting in numerous simulation studies and injection pilots. However, performance from injections pilots has typically been disappointing compared to the simulations, suggesting that reservoir permeability and heterogeneity are not adequately described in the reservoir simulation models. In this study, a simulation and history-matching approach was used to quantify the permeability matrix over a six-section, nine-well area. Twelve years of production data were history-matched, using a combination of pressure-dependent permeability and enhanced permeability to represent natural fractures or other high-permeability features. Also, the performance of a failed injection pilot was history-matched to determine the level of reservoir heterogeneity needed to explain the pilot failure. Based on this study, a reservoir description capable of matching twelve years of production and injection history has been developed. Formation properties in the high-permeability streaks capable of causing the disappointing injection pilot performance have been quantified. Recovery has been forecast to depletion, and EOR under hydrocarbon gas injection has been forecast for a variety of scenarios. Optimal operating strategies and recommendations for technology development to mitigate early breakthrough are made. Realistic cost estimates were made for each scenario, and economics were run for each recovery method. These results give insight into the economic potential of enhanced oil recovery in the Elm Coulee Bakken formation. Recommendations for favorable tax treatment and scheduling of expenses/investments are made. Developing the permeability matrix using the history matching approach is a novel and versatile way of quantifying unconventional reservoir properties. However, it is important to match both injection and production data, since the permeability vector appears to have pressure-dependent effects. The effect of controlling injection thief zones by controlling local wellbore outflow is quantified, and a need for in situ permeability modification of fracture thief zones has been determined.

No abstract

The use of artificial neural networks (ANN) for reservoir analysis now makes it possible to predict important reservoir properties from combinations of data such as well logs, production data, seismic data, etc. In this work, an ANN was combined with a geostatistical linear estimation algorithm in a technique called the hybrid approach, which was used to enhance sparse data to include in a reservoir simulation model with the goal of reducing history matching time. The case study field, Fort Collins Field, is situated on the N-S anticline on the western edge of the Denver Basin in Colorado. The available porosity and permeability data needed to build a reservoir simulation model are old and sparse. Available well logs and cores were used as inputs to the hybrid model. This model was then used to predict porosity and permeability for the reservoir and these values were then included in a reservoir simulation model. To evaluate the hybrid approach, the reservoir simulation model was history matched with the case study historical production data and compared to a model with average data. The result confirms that the hybrid approach history matched better and faster compared to the simple averaging-technique. The history match results from both methods were compared based on the percentage error. This unique approach will benefit older fields with sparse data.

No abstract