Molyama Kromah, Head Technical Solutions, India (TSI) & Director, bp India, is an accomplished oil and gas professional with 25 years of experience at BP, spanning operations across Trinidad, the UK, Egypt, and India. She leads bp TSI, driving engineering and subsurface initiatives while leveraging extensive expertise in upstream operations, gas field development, strategic planning, business transformation, and leadership across the energy value chain.
The article explores the growing role of AI in deepwater exploration, showcasing its impact on exploration accuracy, operational performance, and risk mitigation. It provides insights into how advanced analytics, automation, and predictive technologies are helping operators unlock complex offshore resources while preparing organizations and future leaders for a more data-driven industry.
There's a brutal honesty to deepwater exploration that onshore operations rarely demand. At depths exceeding 3,000 metres, where pressures can exceed 15,000 psi and temperatures swing wildly across geological formations, the margin for error is essentially zero.
A single misread reservoir model or a delayed response to a wellbore anomaly doesn't just cost money — it can cost lives, assets, and years of regulatory recovery. It is precisely this unforgiving environment that has accelerated the adoption of artificial intelligence from an operational experiment to a foundational requirement.
Five years ago, AI in upstream oil and gas sector was largely confined to pilot programs and conference presentations. Today, it is embedded in seismic interpretation workflows, real-time drilling decisions, and predictive maintenance systems across some of the world's most complex offshore assets.
According to a 2023 report by McKinsey & Company, AI and advanced analytics applications in upstream oil and gas could generate between $50 billion and $80 billion in annual value globally, with deepwater operations accounting for a disproportionate share of that opportunity.
The foundation of any deepwater exploration campaign is seismic data consisting of vast three-dimensional surveys of the subsurface that reveal potential reservoir formations, fault lines, and fluid contacts. Traditionally, interpreting this data required teams of geoscientists spending months manually analyzing seismic volumes, often with inconsistent results depending on individual expertise and cognitive bandwidth.
AI-powered seismic interpretation has fundamentally changed that labor intensive process. Machine learning models can now process full-field 3D seismic surveys in hours rather than months, identifying reservoir geometries, salt body boundaries, and stratigraphic traps with a consistency that no human can replicate at scale.
The practical consequence is not that geoscientists become redundant, rather, it is that they are freed from the repetitive and time-consuming work of data processing and their focus can be redirected toward geological reasoning and decision-making — the tasks that genuinely require human judgment. The quality of exploration decisions improves not because AI replaces expertise, but because it amplifies it.
AI is not replacing expertise in deepwater exploration; it is amplifying human judgment, reducing uncertainty, and enabling safer, smarter operational decisions
One of the most commercially significant applications of AI in deepwater exploration lies in reservoir characterization and simulation. Before committing to a well location — often a decision costing $150 million or more for a deepwater appraisal well — operators now routinely run predictive models that simulate reservoir behavior across hundreds of production scenarios.
These models, increasingly informed by machine learning rather than purely physics-based simulation, can integrate seismic attributes, well log data, and fluid sampling results to generate probabilistic estimates of recoverable reserves, pressure depletion patterns, and optimal well placement. The result is a materially better-informed decision at the exploration stage — one that reduces the risk of drilling into low-productivity zones or mischaracterising the extent of a pay zone.
Perhaps the most operationally consequential application of AI in deepwater is real-time monitoring and anomaly detection. Modern deepwater assets generate enormous volumes of sensor data continuously: wellbore pressure, temperature, mud weight, flow rates, equipment vibration signatures, and blowout preventer (BOP) status, among dozens of other parameters. The challenge has never been data collection. It has been making sense of it fast enough to matter.
AI-driven monitoring platforms now interpret this data in real time and apply anomaly detection algorithms to identify warning signals that human operators might miss in the noise. A subtle increase in drilling rate can indicate a risk of well control before any influx enters the wellbore; an unusual vibration pattern can predict stuck pipe early enough to avoid it; or a small pump pressure change can identify downhole equipment damage that is more than 3 km in the well. These are the kinds of signals that, caught early enough, allow for controlled intervention rather than emergency response.
In addition to AI’s use during operations, it has also driven a major step change in well planning. It can analyse large volumes of operational reports from offset wells, highlight key risks to the engineer, and provide an initial set of mitigations and recommendations.
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Beyond data analysis, monitoring, and well planning support, AI is beginning to enable drilling parameter optimization to increase efficiency. Automated drilling systems can now adjust weight on bit, rotary speed, and mud flow rates in real time based on formation feedback, optimizing rate of penetration while staying within safe operating parameters.
This kind of narrow automation does not eliminate the drilling engineer. It removes the cognitive load of managing hundreds of small, data-intensive decisions per hour so that the engineer can focus on the judgments that require contextual understanding, experience, and accountability. In an industry where human fatigue and shift handover gaps have historically contributed to operational incidents, the redistribution of cognitive load carries real safety value.
As AI models become more capable, more interpretable, and more integrated with subsurface and operational datasets, the impact will extend beyond incremental efficiency gains. The real shift is in enabling wells that were previously considered too complex, too uncertain or too high risk to attempt. AI is expanding the operational envelope of what can be drilled safely and economically.
At the same time, AI is fundamentally changing reliability. Continuous monitoring, predict modeling and data driven well planning are reducing non-productive time and preventing failures before they materialize. Events such as well control incidents, stuck pipe and equipment failures are increasingly identified as emerging risks rather than sudden surprises, allowing mitigations to be planned and executed under control.
The result is not just faster drilling, it's predictable delivery, fewer unplanned events, fewer recovery operations and wells that are executed closer to plan.
For those entering or ascending through leadership roles in deepwater operations today, this technological shift presents both an extraordinary opportunity and a genuine test of professional identity. The temptation, particularly for technically trained engineers and geoscientists, is to treat AI as either a threat to domain expertise or, conversely, as a shortcut that removes the need to develop deep technical foundations. Both instincts are mistaken.
The most effective leaders in this space will be those who develop what might be called technical bilingualism — a fluency in both the domain science of subsurface and drilling engineering and the data and modeling principles that underpin AI systems. This does not mean every drilling engineer needs to write machine learning code. It means understanding how a model was trained, what data it was trained on, where its confidence degrades, and when to override it. AI systems in critical environments are only as reliable as the human judgement applied at their boundaries.
Equally important is an understanding of how AI changes team dynamics and organisational accountability. When an anomaly detection system flags a wellbore event and the driller overrides it, who carries the decision? When a machine-generated reservoir model underpins a $200 million commitment, how is that model validated and by whom? Emerging leaders need to be architects of the governance structures that give AI-assisted decisions their integrity — not just users of the tools themselves.
One practical piece of advice: seek exposure to projects at the intersection of data science and operations as early as possible. The engineers who will lead the next generation of deepwater campaigns are not those who avoided AI tools, nor those who adopted them uncritically — they are those who engaged with the technology rigorously enough to know both its power and its limits.
Beyond individual development, there are broader industry-level shifts worth tracking. One of the most significant is the convergence of AI with digital twin technology — the creation of high-fidelity virtual replicas of physical wells and reservoirs that can be updated in real time with operational data. As these digital twins become more sophisticated, they will allow operators to test interventions virtually before committing to them in the wellbore, compressing decision timescales and reducing operational risk in ways that are only beginning to be realized.
There is also a growing recognition that AI's value in deepwater is inseparable from data quality and data governance. The models are only as good as the data pipelines that feed them. Operators who have invested in standardizing their well data, cleaning historical records, and building interoperable data architectures will capture disproportionate value from AI over the coming decade. Those who have not will find their AI programs constrained not by algorithmic limitations, but by the foundational unglamorous work of data management that was deferred.
Finally, the human dimension of this transition deserves more honest conversation than the industry typically gives it. AI does not just change what people do — it changes what they need to know, what they are accountable for, and ultimately how they develop professional judgement over a career.
The deepwater industry has always been defined by the quality of its people operating under pressure. That will remain true. What changes is the environment in which that quality is developed and expressed and how we build the next generation of talent to develop operational experience and judgement.
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