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.
The VP of Exploration questioned whether they were integrating all available data as effectively as possible. No human could synthesize all of it simultaneously. The inherited environment combined application behavior, data movement, user workflows, and operational dependencies. Engineering began by locating authoritative data, integration contracts, control owners, failure behavior, and a reversible acceptance boundary.
The Algorithm leadership confirms this engagement as part of the company’s delivery history. Public wording is limited to the technical narrative while client-sensitive and precision claims complete leadership review.
Machine learning recommendation engine for drill site selection. 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.
The engagement produced a working change to the client’s system or operating workflow. Exact measurements, delivery duration, audit outcomes, and client sentiment are withheld until the corresponding leadership-confirmation items are resolved.
The first call is with a senior engineer.
Tell us the system boundary, operating constraint, and evidence required for acceptance. We'll identify the assumptions and technical questions that should shape the engagement.