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Revolutionizing the Montney Through Physics Based Data Analytics

January 23 @ 11:30 AM - 1:00 PM


Predicting primary and infill well performance accurately is an industry focus with increasing development of unconventional shale plays. Traditionally, physics-based forecasting models require extensive time to generate predictions compared to the time to plan and implement inter-well developments because they require extensive human resources and data. Other, more simplified methods, lack the ability to model well-to-well interference effects, either because they rely on empirical relationships or simplified analytical single-well models. This paper presents an alternative method to construct physics-based type well profiles using deep-learning models trained with pre-run reservoir simulator forecasts. This method improves on traditional approaches by delivering the accuracy of physics-based models in a timely manner.

This session will provide Montney-based case studies with neural network approaches resolving fracture interference. Modeling will provide forecasts of primary and future performance production of case study leases.

From several blind tests of predicted vs. actual production for these leases, key takeaways regarding inter-well spacing vs. single layer development will be provided along with impact of production degradation as observed.

The proposed workflow is applicable to reserves estimation from early appraisal projects with little to no production to full-field development projects based on using ranges of physical parameters. The method is also applicable in diagnosing production inefficiencies and developing remediation strategies rapidly to realize the EUR uplift from operational changes such as lowering effective FBHP.


January 23
11:30 AM - 1:00 PM
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SPE – Calgary Section
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Calgary Petroleum Club
319 Fifth Avenue SW
Calgary, Alberta T2P 0L5 Canada
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