Halliburton Chooses IBM Cloud to Help Cut Oilfield Development Costs

Halliburton, the global oilfield services company that works with many of the world’s largest oil and gas players, has adopted IBM Cloud to run more detailed reservoir simulations, which may help struggling producers cut costs.

Pressure to Cut Costs

Record low oil prices, caused by strong output among oil producing nations amid weaker global demand, have put pressure on producers of the fossil fuel to lower extraction costs. Crude oil prices have fallen by more than half in the past two years, and talks between OPEC and Russia aimed at curbing production have stalled.

“Using high performance computing of the IBM Cloud, we can run very detailed simulation models and evaluate a wide range of field development options, which translates into better field development plans for our clients and a competitive advantage for our business,” Steven Knabe, a Halliburton Consulting director, said in an IBM statement.

The world’s biggest oil companies are expected to report their worst quarterly earnings in more than a decade because the industry’s cost-cutting efforts haven’t yet offset the decline in crude prices, Bloomberg reported. This adds pressure on producers to leverage technology to help plan reservoir development in a way that maximizes returns.

What Reservoir Simulation Does

An oil or gas reservoir isn’t an underground lake. It’s rock with oil, gas and water held in tiny pores, spread across an area that nobody can see directly. Engineers build computer models of that rock from seismic surveys, well logs and production data, then simulate how fluids will flow as wells are drilled and the field is produced.

Those simulations are what a field development plan rests on: how many wells to drill, where to put them, and how fast to produce. Because nobody knows exactly what’s underground, a good plan tests many versions of the reservoir and many development options, not just one. Each run takes a lot of computing power, which is why on-demand cloud capacity is useful. A company can rent a large amount of computing for a big study, then stop paying for it when the work is done.

Data and the Oil Patch

Like many industries, oil and gas companies are turning to technical advances available through cloud computing, data analytics, and machine learning to boost productivity.

And there is room for growth. “While some oil and gas companies have invested in their analytics capabilities, many struggle to get their arms around this powerful new opportunity,” consulting firm Bain & Company said in a report titled “Big Data analytics in oil and gas.”

Adopting IBM Cloud technology allows Halliburton to “quickly run hundreds of simulation cases to forecast the possible behavior of complex oil and gas fields,” according to the IBM statement, which doesn’t disclose terms for the company’s usage of IBM Cloud.

IBM markets a range of solutions for enhancing production, improving processing efficiency, and optimizing global operations for the oil and gas industry.

Halliburton Consulting specializes in formulating development plans for both new and mature fields.

Where It Led

The idea of running many simulation cases instead of one has since become central to how Halliburton sells its software. In June 2025, Halliburton announced a collaboration with PETRONAS Carigali, the Malaysian state oil company’s exploration and production arm, to deploy its Landmark DecisionSpace 365 Geosciences Suite and Unified Ensemble Modeling tools. Halliburton said the ensemble approach lets teams automatically generate multiple probabilistic geological scenarios and bring in real-time reservoir flow data, and described it as a significant step on from deterministic reservoir forecasting. PETRONAS said the new approach would include modeling supported by AI and machine learning.

Automation has moved from planning into the field, too. In March 2026, Halliburton said that, working with ExxonMobil, Sekal, Noble and the Wells Alliance Guyana team, it had achieved the industry’s first fully automated geological well placement with complete rig automation offshore Guyana. According to Halliburton, the reservoir section finished about 15% ahead of plan and tripping operations took about 33% less time.

That’s a long way from the 2016 announcement, which was essentially about renting more computing power. But the goal is the same one Knabe described: use data and computing to make better development decisions, and to spend less doing it.

What It Means for Tech Companies

In this case, the push to adopt new technology came when money was tight, not when it was plentiful. Halliburton’s move to the cloud came during an oil price slump, when every cost was under scrutiny and better planning had a direct payoff.

For startups selling into energy, mining or manufacturing, that’s a useful pattern. Pitch the cost saving or the better decision, not the technology itself. And expect the bigger opportunity to come later, once the customer’s teams trust the data enough to let software take on more of the work.