Risk management
As drilling operations become increasingly digital, computer vision is emerging as a continuous safety layer that complements, not replaces, established safety practices. The challenge is no longer whether artificial intelligence can detect hazards, but how operators should deploy, integrate, and govern these systems to improve safety without adding operational comple…
This article from the SPE Integrated Reservoir Management Technical Section (IRMTS) addresses decisions that look stronger than they are and take longer to implement than the reservoir can afford.
This paper explores how artificial intelligence (AI), cognitive models, and integrated digital tools can transform readiness from periodic drills into a continuous, measurable capability.
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In spite of massive investments in project management best practices and the organizations to implement them, major oil and gas projects continue to experience cost overruns and schedule delays. A root cause that has not been sufficiently explored is the built-in bias toward overconfidence. -
In this paper, a data-driven model is applied to derive optimum maintenance strategy for a petroleum pipeline. The model incorporates structured expert judgment to calculate the frequency of failure, considering various failure mechanisms.
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Cost estimation for facilities depends on early concept selection and critical inputs, often complicated by uncertainty in one or more of the critical inputs. Empirical cost models and cost modeling methods using these inputs vary in degrees of scope, comprehensiveness, and robustness.
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Engineers in the oil and gas industry make tough decisions for a wide variety of issues, including risk and safety, and about design and other types of tradeoffs, as well as operational assessments.
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