Integrated reservoir characterization and simulation of a shallow, light-oil, low-temperature reservoir: Umiat field, National Petroleum Reserve, Alaska

Hanks, Catherine L.; Shimer, Grant; Kohshour, Iman Oraki; Ahmadi, Mohabbat; McCarthy, Paul J.; Dandekar, Abhijit; Mongrain, Joanna; Wentz, Raelene

doi:10.1306/08201313011

Cited by 4 publications

(1 citation statement)

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“…The Umiat Oil Field of Alaska is one of the largest, undeveloped light-oil fields in the United States, with an estimated 1 billion barrels of original oil in place within the Cretaceous Nanushuk Formation [10] (Figure 1). The Umiat field was first discovered in 1946 and remains largely undeveloped over the past 70 years due to a lack of infrastructure at Umiat.…”

Section: Umiat Oil Fieldmentioning

confidence: 99%

Machine Learning-Based Probabilistic Lithofacies Prediction from Conventional Well Logs: A Case from the Umiat Oil Field of Alaska

Dixit

McColgan²,

Kusler

2020

Energies

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A good understanding of different rock types and their distribution is critical to locate oil and gas accumulations in the subsurface. Traditionally, rock core samples are used to directly determine the exact rock facies and what geological environments might be present. Core samples are often expensive to recover and, therefore, not always available for each well. Wireline logs provide a cheaper alternative to core samples, but they do not distinguish between various rock facies alone. This problem can be overcome by integrating limited core data with largely available wireline log data with machine learning. Here, we presented an application of machine learning in rock facies predictions based on limited core data from the Umiat Oil Field of Alaska. First, we identified five sandstone reservoir facies within the Lower Grandstand Member using core samples and mineralogical data available for the Umiat 18 well. Next, we applied machine learning algorithms (ascendant hierarchical clustering, self-organizing maps, artificial neural network, and multi-resolution graph-based clustering) to available wireline log data to build our models trained with core-driven information. We found that self-organizing maps provided the best result among other techniques for facies predictions. We used the best self-organizing maps scheme for predicting similar reservoir facies in nearby uncored wells—Umiat 23H and SeaBee-1. We validated our facies prediction results for these wells with observed seismic data.

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Section: Umiat Oil Fieldmentioning

confidence: 99%