This paper presents how Artificial Intelligence (AI) / Machine Learning (ML) technology uses unsupervised genetics algorithms in Exploration, Drilling Operations, Field Appraisal, Development and multiple 3D seismic volumes comparisons to minimize geological risk and uncertainty resulting in increased capital efficiency. We will present a high level overview of why this technology was invented and how it works. We will show you how you can use it to significantly reduce the time to achieve your organizational goals while reducing geotechnical risk and uncertainty and optimize the cycle time from Lead to Production. Outputs include a comprehensive analysis of your entire 3D Seismic Data Volume to identify and high grade, quality leads and prospects with high resource potential in the near, medium and long term. This approach will allow an evaluation of the field geological risk (reservoir distribution, trap, seal, source, hydrocarbon migration pathway from source into reservoir) and initial possible hydrocarbon content/type evaluation (e.g. DHI evaluation) without disrupting your current workflow. The results will quickly delineate possible structural and stratigraphic targets. This will also provide the Production Asset with additional support in their appraisal and development drilling programmes. Optimally place horizontal wells and injectors / offtakes in Improved Oil Recovery/Enhanced Oil Recovery (IOR / EOR) projects in areas of the field having the highest reservoir continuity to optimize the cycle time from concept to production. The case studies and examples presented will demonstrate how the technology and approach serve to increase the probability of success leading to increased capital efficiency and profitability.

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