A mid-size exploration and production company operating across the Permian Basin in West Texas. Dozens of active wells, hundreds of potential drill sites. Their geologists were evaluating sites using seismic data, well logs, production history, and professional judgment. Good results — but expensive judgment calls. A dry well costs $3-8M.
Three consecutive underperforming wells in a six-month period. Combined investment of $18M with returns tracking 40% below projections. The VP of Exploration questioned whether they were integrating all available data as effectively as possible. They had 30 years of production data, seismic surveys, and geological assessments. No human could synthesize all of it simultaneously.
They needed ML engineers who could work with geological data — not a generic data science shop that would spend six months understanding the domain. Our team came in with both the engineering capability and enough domain exposure to speak the language.
Machine learning recommendation engine for drill site selection. Ingested and normalized 30 years of production data, 3D seismic surveys, well log data, geological assessments, and satellite-derived surface indicators. Feature engineering extracted 140+ geological and production attributes per candidate site. Ensemble model (gradient boosted trees + neural network) trained on historical production outcomes to predict probability of commercial production and estimated recovery volume. Output: ranked list of candidate sites with confidence scores, estimated production profiles, and the specific geological features driving each recommendation.
Model accuracy: 87% of recommended sites achieved commercial production — compared to 68% historical average. First three wells drilled on ML recommendations outperformed basin averages by 34%. The company drilled fewer wells but produced more oil. The VP of Exploration's assessment: 'It doesn't replace the geologist. It makes the geologist smarter.'
The first call is with a senior engineer.
Tell us the industry, the regulatory environment, and what needs to be built. We'll tell you if we've done it before, what it should cost, and how long it takes.