Conocophillips Company

Benjamin Lascaud

Khalid A Soofi

Phuong Hoang

Patrick R Boyle

Thomas James Wheeler

Hui Zhou

Julian Ortiz Arango

Harish T Kumar

Gustavo A Gomez

Mohammad H Kalaei

A system and method of detecting subsurface karst features includes receiving surface mapping data. A potential surface pad location can be identified in view of the surface mapping data. A resistivity survey for the potential surface pad location can be designed. The resistivity survey can include at least one long line extending through a surface hole for each of one or more wellbores in the potential surface pad location, and a short line extending through the surface hole of one of the one or more wellbores, each short line intersecting the long line substantially at the surface hole of one of the one or more wellbores. High resistivity areas exceeding approximately 150 Ohm per meter can be identified as sub surface karst features within the resistivity survey.

Obstacle detection systems and methods

The present disclosure generally relates to harvesting geothermal energy from mature and near end-of-life oil and gas reservoirs that have been subjected to secondary oil recovery steam processes like steam-assisted gravity drainage (SAGD), steamflood, etc. The geothermal potential of these mature SAGD reservoirs can be used to generate green electricity thus reducing the greenhouse gas (GHG) footprint of the oil production. Lateral spacing of injectors and producers, with closing of unused members of a well-pair for energy recovery is described.

Heat harvesting of end-of-life wells

Implementations described and claimed herein provide systems and methods for developing resources from an unconventional reservoir. In one implementation, raw reservoir data for the unconventional reservoir is obtained. The raw reservoir data includes geology data, completion data, development data, and production data. The raw reservoir data is transformed to transformed data. The raw reservoir data is transformed to the transformed data based on a transformation from a set of one or more raw variable to a set of one or more transformed variables. The set of one or more transformed variables is statistically uncorrelated. Resource development data is extracted from the transformed data. Performance analytics are generated for the unconventional reservoir using the resource development data. The performance analytics are generated through ensemble machine learning. The unconventional reservoir is developed based on the performance analytics.

Integrated machine learning framework for optimizing unconventional resource development

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Houston, TX, United States of America

Benjamin Lascaud