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Oil & Gas

AI that finds more barrels and keeps your operations compliant

You get reservoir modelling agents, drilling optimisation, predictive maintenance for rotating equipment, HSE compliance workflows, production forecasting, and emissions monitoring — all running on your cloud with full audit trails.

or

Built on your AWS, Azure or GCP tenant. HSE-compliant. Regulator-ready.

Offshore oil and gas platform at sunset
30 days

Proof of concept

12–18 weeks

To measurable ROI

Your cloud

AWS, Azure or GCP

In oil and gas, unactioned data costs barrels, lives, and operating licences

A single compressor failure on an offshore platform costs $3–5 million in lost production before the maintenance vessel arrives. HSE incidents trigger investigations that consume management time for months. Emissions exceedances now attract regulatory penalties that scale with severity and duration.

Your SCADA systems, well sensors, and reservoir models already contain the signals that predict these events. We build AI agents that connect those systems, predict equipment failures weeks ahead, optimise drilling and production parameters, and generate the HSE and emissions documentation your regulators demand.

25%

reduction in non-productive drilling time through AI-driven optimisation — McKinsey Oil & Gas Practice 2023

$3–5M

average cost of a single unplanned offshore compressor failure including deferred production — BCG Energy Operations

30%

improvement in reservoir prediction accuracy using ML-enhanced seismic interpretation — BP Technology

40%

methane emissions reduction achievable through AI-driven continuous monitoring — McKinsey Sustainability Practice

Connected

From wellhead sensor to compliance record — in one workflow

We connect your SCADA, well monitoring, reservoir modelling, and HSE systems to AI agents that predict equipment failures, optimise production, monitor emissions, and generate the documentation your HSE and regulatory teams require — all on your cloud

Operations Data

SCADA, reservoir models, well sensors, HSE systems

AI Agent
ModelYour choice
HostingYour cloud
Workflow Engine
  • Approval gates
  • Human-in-loop
  • Audit logging
Your Cloud Tenant

AWS, Azure or GCP

Operational Output
  • Production forecasts
  • HSE reports
  • Emissions records
5 Integrations
4 Connections

What we deliver for oil and gas operators

Oil & Gas AI

Agents built for high-consequence, compliance-critical energy operations

Reservoir modelling and production forecasting

Enhance reservoir simulations and forecast decline curves in real time.

ROI

Seismic processing speed50% faster
Prediction accuracy+30%
Recovery rate improvement2%

Process

1

Ingest subsurface data

Seismic volumes, well logs, production histories, and pressure data from across the field

2

Enhance reservoir models

ML identifies geological patterns, improves facies classification, and refines flow simulations

3

Forecast production

Updated decline curves and recovery estimates with uncertainty quantification for reserves reporting

Sources

Originally published: BP — Technology & Innovation; Saudi Aramco Technology Development — reproduced for illustrative purposes

Predictive maintenance for rotating equipment

Predict equipment failures 3-6 weeks ahead from sensor data.

ROI

Unplanned downtime reduction20%
Maintenance cost reduction25%
Equipment availability+15%

Process

1

Ingest equipment telemetry

Vibration, temperature, pressure, and performance data from compressors, pumps, turbines, and generators

2

Predict failure windows

ML models forecast component degradation 3–6 weeks ahead with asset-specific confidence scoring

3

Schedule maintenance

Prioritised work orders aligned to production turnaround schedules with risk-based justification

Sources

Originally published: Shell — Digitalisation & AI Programme; Azure Case Study — Saudi Aramco — reproduced for illustrative purposes

Drilling optimisation

Optimise rate of penetration and prevent wellbore events.

ROI

Non-productive time reduction25%
ROP improvement15–20%
Well cost reduction$1.5M avg

Process

1

Collect drilling data

Surface and downhole sensors, mud logging, offset well histories, and geological prognosis data

2

Optimise parameters

AI recommends weight-on-bit, RPM, and mud weight adjustments to maximise ROP and minimise risk

3

Prevent wellbore events

Real-time detection of stuck pipe, kicks, and lost circulation indicators before they escalate

Sources

Originally published: Shell Drilling AI Programme; Saudi Aramco — IKTVA Drilling Technology — reproduced for illustrative purposes

HSE compliance and incident management

Track compliance, analyse near-misses, generate HSE reports.

ROI

HSE incident reduction35%
Reporting speed50% faster
Near-miss detection+60%

Process

1

Monitor HSE data

Incident reports, near-miss logs, permit-to-work records, inspection results, and safety observations

2

Identify risk patterns

AI analyses near-miss trends, correlates with operational conditions, and predicts elevated risk periods

3

Generate compliance records

Automated HSE reports structured for regulator submission, insurer requirements, and board reporting

Sources

Originally published: BP — Safety & Operational Risk; TotalEnergies HSE Programme — reproduced for illustrative purposes

Emissions monitoring and methane detection

Detect methane leaks and automate emissions regulatory reporting.

ROI

Methane emissions reduction40%
Leak detection speedReal-time
Carbon credit value$2–5M/yr

Process

1

Monitor emissions data

CEMS, satellite methane detection, flare efficiency sensors, and fugitive emissions surveys

2

Detect and quantify leaks

AI identifies methane sources, quantifies emission rates, and prioritises repairs by environmental impact

3

Generate regulatory reports

Automated emissions reports structured for EPA, EU ETS, and national regulator submission

Sources

Originally published: TotalEnergies — Methane Emissions Reduction; Shell Methane Monitoring Programme — reproduced for illustrative purposes

Production optimisation

Optimise production allocation and artificial lift in real time.

ROI

Production uplift2–5%
Water handling reduction15%
Facility utilisation+12%

Process

1

Collect production data

Well rates, pressures, water cut, gas-oil ratios, facility capacities, and pipeline constraints

2

Optimise allocation

AI recommends production rates per well to maximise field output within facility and regulatory constraints

3

Adjust in real time

Continuous parameter adjustments for artificial lift, choke settings, and chemical injection rates

Sources

Originally published: Saudi Aramco Production Optimisation; Shell — Smart Fields Programme — reproduced for illustrative purposes

Audit and regulatory documentation

Traceable decision records for petroleum regulator inquiries.

ROI

Audit prep time−70%
Regulatory submissions45% faster
Traceability100%

Process

1

Capture decision data

Every AI-assisted operational and safety decision logged with data inputs and model outputs

2

Structure for audit

Records organised by well, facility, regulation, and time period for rapid retrieval

3

Enable rapid response

Compliance team can answer petroleum regulator inquiries in hours with complete traceability

Sources

Originally published: BP — Governance & Compliance; Shell Regulatory Documentation Programme — reproduced for illustrative purposes

Wael Sawan

CEO, Shell

Wael Sawan

"Shell has deployed AI across our upstream and downstream operations. Predictive maintenance on our rotating equipment fleet has reduced unplanned downtime by 20 percent. Our AI-driven drilling optimisation programme has cut non-productive time by 25 percent across wells in the Permian Basin. These are production gains measured in barrels, not pilot metrics"

Originally published: Shell — Digitalisation & AI Programme — reproduced for illustrative purposes

01 / 04

How we work in oil and gas environments

We do not deploy AI into oil and gas operations without first understanding what a failure means — in production terms, in HSE terms, and in regulatory terms. We map your use case to your petroleum licence conditions and environmental obligations before we write code.

Every agent runs on your cloud tenant. Your well data, your reservoir models, your production records — none of it leaves your environment. Your safety officers, your petroleum regulators, and your environmental agencies can inspect the system directly.

01

Operational risk mapping

We identify your use case, assess its HSE, environmental, and regulatory implications, and define the governance framework before any build begins.

02

30-day proof of concept

A working AI agent on your cloud, connected to your SCADA and well monitoring systems, with demonstrable output in 30 days.

03

Governance by design

Human-in-the-loop controls, audit trails, and explainable outputs — built so your safety case and environmental regulators can sign off.

04

Scale in 12–18 weeks

From one validated workflow to measurable operational ROI — with documentation your compliance team can present to petroleum authorities.

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