The end of the second quarter is a travelling season in my business, and this year's circuit from Adelaide to Perth to Brunei carried an unfamiliar and better mood. What my kids would call the 'vibe' had shifted. Political events, firmer oil, gas and LNG prices and renewed concern for energy security have lifted spirits across the upstream industry. After a decade out of fashion, the subsurface and its role in keeping the lights on was once again on the lips of investors, politicians and regulators.
Looking closer, I was struck by a paradox. Just as oil and gas capability returns to the agenda of decision-makers, a decade of steadily narrowing the subsurface ecosystem has weakened its ability to answer the call. This is not a story of decline. It is a story of reconfiguration and one with consequences for any revival in activity.
Over the past decade, upstream companies have repeatedly reshaped their subsurface organisations in response to portfolio maturity, capital discipline and technological change. In isolation, each decision was rational. As investors pressed for a faster energy transition and commodity prices stayed low, exploration declined and development portfolios were standardised. The economic case for large technical teams evaporated, and capability was rebalanced towards asset optimisation, digital workflows and outsourced specialist services. bp's leadership captured the mood, describing the aim of focusing its “highly skilled workforce on only the highest margin barrels”. The language across the sector was consistent: leaner, simpler, more focused.
Efficiency at the level of the organisation does not equal resilience at the level of the system.
But subsurface capability is not held by operators alone. It is co-produced across an ecosystem of service companies, research institutions and collaborative consortia, and that ecosystem read the signals the market sent it. As demand for frontier exploration, basin-scale studies and early-life uncertainty fell, parts of the services sector consolidated or disappeared. Nowhere is this clearer than in geophysical acquisition, where falling activity has cut the number of large-scale marine seismic players to two. This is not a failure of the service industry; it is an efficient response to structural change. But it leaves a thin layer of capacity concentrated on the most certain work.
A similar pattern has played out in research. Pre-competitive geoscience and industry-supported consortia have been squeezed as discretionary spending dried up and public funding moved towards non-hydrocarbon energy. Where money remained, it favoured applied modelling, machine learning and emissions reduction over basin-scale understanding and long-horizon science. Rational capital allocation, certainly but it quietly shifts the balance from foundational advance to incremental gain.
The combined effect is a system in which subsurface capability has been narrowed rather than nurtured. Operators retain talent, but in a tightly defined context. Service companies retain expertise, but with less exposure to risk. Researchers pursue low-risk problems with short feedback cycles. Each part is efficient in isolation; collectively, the system is less able to sustain the long learning cycles that build deep capability. There is the central tension: efficiency at the level of the organisation does not equal resilience at the level of the system.
None of this is a criticism. These shifts were understandable and, in many cases, necessary. The more interesting question, as we stand perhaps on the cusp of an upturn, is what they have cumulatively produced. Subsurface work is a cumulative, integrated discipline: intuition and transferable insight are forged through exploration, regional synthesis and iterative learning across basins and plays. When those activities contract, the feedback loops that generate them weaken.
The question, then, is not whether companies should reverse course. It is whether today's model of capability formation suits a sector that depends on exploiting natural anomalies through informed risk-taking. Judgement that artificial intelligence can support but not replicate. Efficiency has been achieved. The task now is to build resilience alongside it.
Simon Molyneux is Managing Director of Molyneux Advisors.
Views expressed in this column are solely those of the author.
Image courtesy of CSIRO
