English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Well known oil recovery factor estimation techniques such as analogy,volumetric calculations, material balance, decline curve analysis, hydrodynamicsimulations have certain limitations. Those techniques are time-consuming,require specific data and expert knowledge. Besides, though uncertaintyestimation is highly desirable for this problem, the methods above do notinclude this by default. In this work, we present a data-driven technique foroil recovery factor estimation using reservoir parameters and representativestatistics. We apply advanced machine learning methods to historical worldwideoilfields datasets (more than 2000 oil reservoirs). The data-driven model mightbe used as a general tool for rapid and completely objective estimation of theoil recovery factor. In addition, it includes the ability to work with partialinput data and to estimate the prediction interval of the oil recovery factor.We perform the evaluation in terms of accuracy and prediction intervalscoverage for several tree-based machine learning techniques in application tothe following two cases: (1) using parameters only related to geometry,geology, transport, storage and fluid properties, (2) using an extended set ofparameters including development and production data. For both cases modelproved itself to be robust and reliable. We conclude that the proposeddata-driven approach overcomes several limitations of the traditional methodsand is suitable for rapid, reliable and objective estimation of oil recoveryfactor for hydrocarbon reservoir.

Machine learning for recovery factor estimation of an oil reservoir: a tool for de-risking at a hydrocarbon asset evaluation