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Oil and Gas Drilling Optimization

AI-driven optimization of drilling operations including location selection, real-time drilling parameters, well production, and field development planning.

The Problem

“Optimize drilling and field development decisions in real time across the well lifecycle”

Organizations face these key challenges:

1

Real-time drilling decisions depend heavily on scarce expert judgment

2

Operational data is fragmented across rig systems, historians, WITSML feeds, and engineering applications

3

Static drilling plans do not adapt well to changing downhole conditions

4

Manual well assessment misses local geological variation and offset-well nuance

5

Inconsistent setpoint selection causes avoidable inefficiency and risk

6

Production optimization is often disconnected from drilling and completion decisions

7

Field development plans are updated too slowly as new subsurface information becomes available

8

Teams lack trustworthy AI recommendations with clear operational guardrails and auditability

Impact When Solved

Reduce nonproductive time through earlier detection of drilling dysfunctions and prescriptive response recommendationsImprove rate of penetration and drilling efficiency with optimized weight on bit, RPM, flow rate, and mud parametersIncrease consistency of drilling decisions across crews, rigs, and assetsImprove well placement and trajectory decisions using local geological and offset-well intelligenceEnhance production forecasting and field development planning under subsurface uncertaintySupport progression from advisory systems to closed-loop optimization and autonomous operations

The Shift

Before AI~85% Manual

Human Does

  • •Review offset wells, geology, and drilling program to choose locations and parameter windows
  • •Monitor rig dashboards, alarms, and daily reports to detect dysfunctions during drilling
  • •Adjust WOB, RPM, flow, and mud practices through driller and engineer judgment
  • •Coordinate decisions across rig, office, and service providers during operational events

Automation

  • •Basic alarms flag threshold breaches from surface and downhole measurements
  • •Dashboards summarize live drilling data and daily performance metrics
  • •Static reports compile historical well data for manual comparison
  • •Rule-based monitoring highlights obvious deviations from the drilling program
With AI~75% Automated

Human Does

  • •Approve drilling objectives, operating limits, and risk tolerance for each section
  • •Accept, modify, or reject AI-recommended setpoint changes and mitigation actions
  • •Handle exceptions, safety-critical events, and cross-team tradeoff decisions during operations

AI Handles

  • •Continuously monitor high-frequency drilling, geology, and equipment data for weak signals of dysfunction
  • •Predict risks such as stick-slip, whirl, pack-off, lost circulation, stuck pipe, and kicks before escalation
  • •Recommend optimal drilling parameters to balance ROP, equipment wear, and operational risk in real time
  • •Prioritize alerts and surface contextual actions for rig and office teams

Operating Intelligence

How Oil and Gas Drilling Optimization runs once it is live

AI runs the first three steps autonomously.

Humans own every decision.

The system gets smarter each cycle.

Confidence95%
ArchetypeRecommend & Decide
Shape6-step converge
Human gates1
Autonomy
67%AI controls 4 of 6 steps

Who is in control at each step

Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

Loop shapeconverge

Step 1

Assemble Context

Step 2

Analyze

Step 3

Recommend

Step 4

Human Decision

Step 5

Execute

Step 6

Feedback

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

The Loop

6 steps

1 operating angles mapped

Operational Depth

Technologies

Technologies commonly used in Oil and Gas Drilling Optimization implementations:

Operational decision support interfaceOther
3 mentions
Real-time drilling data feedsOther
3 mentions
DrillOps advisoryOther
2 mentions
DrillOps automationOther
2 mentions

Key Players

Companies actively working on Oil and Gas Drilling Optimization solutions:

Baker HughesHalliburtonSLBAspenTech

Real-World Use Cases

APOLO for drilling location optimization and production forecasting

Chevron uses an AI system to study millions of well and geology data points so engineers can better predict how a new well will perform and choose better places and designs for drilling.

Predictive analytics and optimization decision supportdeployed in chevron operations in the permian and dj basins, with planned expansion across global shale and tight assets.
10.0

AI-guided drilling setpoint and trajectory optimization

The system suggests better drilling settings and paths while the well is being drilled so crews can drill more efficiently.

prescriptive optimizationproposed/deployed product capability presented as built-in platform intelligence.
10.0

Predictive drilling optimization for smart well operations

AI watches live drilling data and helps crews make better drilling decisions faster, like a co-pilot that spots problems early and suggests how to drill more efficiently.

real-time prediction and operational decision supportdeployed commercial solution presented as an active platform capability and case study.
10.0

Real-time drilling decision support with DrillOps advisory

An AI advisor watches live drilling data and tells crews the best next action so they can drill faster and avoid problems.

Real-time advisory and decision supportdeployed commercial solution highlighted by slb as part of current drilling operations offerings.
10.0

AI-driven drilling optimization for upstream wells

AI acts like a smart co-pilot for drilling teams, using live well data to suggest how to drill faster and more safely.

Real-time optimization and decision supportproposed/high-priority use case in an ai-first upstream operating model, but the source does not confirm broad production deployment.
10.0
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