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ENB Pub Note: This article was originally posted on LinkedIn by Jon Brewton, Founder and CEO of the AI company Data Squared. https://data2.ai/

I had the privilege of interviewing Jon in the first of our AI in Energy Series, and you can see that here: AI’s Impact on Energy: Optimizing Data and Decision-Making with Jon Brewton and Mark Stansberry

Artificial intelligence has moved from a demo-room curiosity to a profitand-safety engine for oil-and-gas operators. In upstream it squeezes more barrels out of reservoirs with fewer interventions; in midstream it keeps hydrocarbons moving safely, cheaply, and on spec. Yet many firms still stall at the “cool pilot” stage and never see bottom-line impact at the well pad or compressor station.

Having worked shoulder-to-shoulder with drill-site leaders, production engineers, pipeline control centers, and trading schedulers, I can say with confidence: AI is not “just another model.” It is a capability that solves real dollar-generating problems, reduces unplanned downtime, schedules cost efficient execution schedules, optimizes production efficiency, and streamlines shipping / delivery issues.

From Hype to Impact, O&G Edition

Successful adopters share three habits:

1. Anchor every use case to a barrel, BTU, or dollar: “Cut subsea pump trips 40%,” “shave $5MM per year in compressor fuel,” “reduce lease operated expense risk by 25%,” “extend ESP run-life by 60% reducing intervention costs.”
2. Fix data quality before model quality: SCADA tags, historian data, vibration signals, corrosion coupons, bottomhole pressure measurements, and fluid analysis data all must roll into a clean digital twin. Garbage in still equals garbage out.
3. Embed AI where work happens: Drillers see real-time weight-on-bit recommendations inside their consoles; production engineers get ESP optimization alerts in Production Optimization systems; pipeline controllers receive leak-probability scores in their SCADA HMI; schedulers receive optimal batch sequences.

AI in Action, Upstream & Midstream Use Cases

Core Upstream Applications – Data² reView Platform Solutions

Lease & Asset Acquisition

    Lease/Block Acquisition Advisor: OCR-enabled document processing, entity recognition for lease terms, knowledge graph visualization of acquisition opportunities with ranking system

    Value: 60-80% faster lease review process, automated audit trails, data-driven ranking of acquisition targets

Well Performance & EUR Optimization

    Lost Production Opportunity Advisor: Daily rate analysis across well types, completion design optimization, field development planning with surface facility requirements

    Value: 8-15% EUR improvement, $200K-500K additional NPV per well through optimized designs

Cost Management & LOE Reduction

    Lease Operating Expense Advisor: AI-driven expense tracking, lease agreement analysis, cost-saving measure identification across field operations

    Value: 15-25% LOE reduction, automated expense categorization, predictive cost modeling

Well Lifecycle Management

    Well Abandonment Advisor: Multi-source data synthesis (production, drilling, workover) with comprehensive abandonment scoring for regulatory compliance

       Value: 20-30% reduction in premature abandonments, optimized

P&A timing, regulatory compliance automation

Drilling & Completion Optimization

    Well and Completion Design Advisor: Performance clustering by design type, best practice identification, optimal configuration recommendations for specific field conditions

    Value: 15-25% drilling efficiency improvement, completion design standardization, reduced trial-and-error costs

Water & Environmental Management

    Water Management Advisor: Basin-wide water network optimization, multi-source coordination (produced/fresh water), transportation cost minimization

    Value: 25-40% water management cost reduction, optimized completion scheduling, reduced trucking expenses

Production & Artificial Lift

    Production Optimization & Artificial Lift Advisor: Real-time production analysis, artificial lift selection, pump optimization, maintenance scheduling

    Value: 40-60% ESP run-life extension, 5-12% production uplift, 30% reduction in lift system failures

Data Quality & Intelligence Foundations

Well Data Integrity

    Well Data Quality Assessment: Scorecards tied to specific data elements, rule-based quality scoring, well clustering by type and completion

    Impact: Foundation for all AI applications, 50-70% reduction in data preparation time, improved model accuracy

Directional Data Accuracy

Directional Drilling Data Assessment: Error identification in survey data, toolface angle validation, correction model application

Impact: Accurate wellbore positioning, reduced drilling risks, improved completion placement

Comprehensive Well Intelligence

Well reView: AI-powered knowledge harvesting, well knowledge diagrams, hidden risk identification from historical records

Impact: Intelligence agency-level well understanding, optimized workover planning, risk mitigation

Strategic AI Implementation

AI Strategy Development

Generative AI Strategy: Opportunity identification, manual task reduction, explainable AI implementation with transparency focus

Value: Clear AI roadmap, measurable ROI targets, reduced implementation risk

Operations Assessment

AI Portfolio Assessment: Subject matter expert evaluation, operation analysis, tailored AI project recommendations

Value: Prioritized AI initiatives, aligned business goals, expert-guided implementation

Data Foundation

Well Data Strategy: World-class data management, governance frameworks, AI-readiness preparation

Value: Unlocked data potential, advanced AI reasoning capabilities, future-proof data architecture

Midstream & Infrastructure Applications

Asset Integrity

Fiber-optic & acoustic ML for early leak detection; hydrogen sulfide concentration prediction in produced gas streams

Value: Detect pinhole leaks within minutes; automated well shut-in when H2S exceeds 100 ppm

Predictive Maintenance

Vibration/pressure analytics for compressors, pumps, and valves integrated with OSISoft PI systems

Value: 20-40% cut in unplanned downtime

Flow Assurance

AI-driven slugging and hydrate-formation forecasting; choke management integrated with separator pressure optimization

Value: Fewer shutdowns, optimized inhibitor dosing, 3-8% throughput gains Logistics & Scheduling

Optimal crude/condensate batch sequencing; tanker demurrage avoidance

Value: Millions saved in penalties and better utilization

Emissions & Flaring

NLP extraction of flare/vent data; ML to predict high-emit events hours ahead for automated carbon hedging

Value: Compliance readiness, reduced carbon intensity

How Data² Transforms Upstream Operations: From Isolated Models to Integrated Intelligence

While traditional AI implementations in upstream O&G often struggle with data silos and black-box outputs, Data²’s reView platform solves the fundamental challenge that keeps most operators stuck in pilot purgatory: connecting disparate operational data into a unified, explainable intelligence layer.

The Upstream Data Integration Challenge

Most operators struggle with fragmented data ecosystems: drilling data lives in Halliburton LOGIX, production data sits in OSISoft PI, geology resides in Petrel, and compliance reporting scattered across Excel sheets.

Traditional AI models work on these data sources in isolation, missing critical connections that drive operational decisions.

Data² solves this with graph based data unification that creates a living digital twin of your entire upstream operation:

    Real-time well performance monitoring: ESP current signatures, bottom hole pressure trends, and fluid analysis data automatically connect to create comprehensive well health profiles

    Drilling-to-production continuity: Completion design parameters flow seamlessly into production optimization models, enabling EUR predictions based on actual drilling performance

    Cross-well insights: Frac hits, interference patterns, and reservoir connectivity emerge from integrated pressure monitoring and microseismic data across your entire field

Explainable AI for Regulatory Confidence

Unlike black-box solutions that generate recommendations without explanation, Data²’s AI outputs come with complete transparency, critical for regulatory compliance with Texas Railroad Commission and EPA requirements.

Every recommendation includes:

Data provenance: Which sensors, wells, and measurements contributed to each insight

Confidence scoring: Risk assessment based on data quality and model validation

Alternative scenarios: “What-if” analysis showing how different operational parameters affect outcomes

Regulatory traceability: Audit trails meeting API 653/570 standards and state compliance requirements

Multi-Modal Operations Intelligence

Data²’s platform ingests and connects every data type in upstream operations:

Structured data: SCADA tags, production reports, drilling logs, fluid analysis

Unstructured data: Daily drilling reports, completion reports, vendor recommendations, regulatory filings

Time series data: Real-time sensor feeds from ESPs, gas-lift systems, surface equipment

Multimedia: Well logs, core photos, seismic images, drone inspection footage

This creates operational insights that would nearly impossible with singlesource AI models:

    Predictive workover planning: Combines decline curve analysis, equipment health data, and economic models to rank intervention priorities

    Drilling hazard prediction: Integrates offset well data, geological reports, and real-time drilling parameters to predict differential sticking hours before it occurs

    Production optimization: Links reservoir models, completion data, and real-time performance to automatically adjust artificial lift settings

Cloud-Agnostic Architecture for Field Operations

Data²’s microservices architecture deploys across any infrastructure, from edge computing at remote well sites to enterprise cloud environments:

    Edge deployment: Critical models run locally at drilling rigs and production facilities, ensuring zero-latency decision support even with intermittent connectivity

Hybrid operations: Non-critical analytics run in cloud while safetycritical models operate on-premises

Legacy integration: Seamless connection to existing Weatherford,

Schlumberger, and Halliburton systems without rip-and-replace

Continuous Learning from Field Operations

Traditional AI models become stale as reservoir conditions change. Data²’s continuous learning engine adapts models based on actual field performance:

ESP models retrain automatically as reservoir pressure depletes and fluid properties change

Drilling models incorporate lessons learned from each well to improve performance on subsequent wells

Completion optimization evolves based on actual EUR performance versus predicted outcomes

Concrete Upstream Value Creation

For Drilling Operations:

Real-time ROP optimization with explainable recommendations showing exactly which parameters to adjust and why

Stuck pipe prediction with confidence intervals and alternative drilling strategies

Automated MSE monitoring with bit-specific performance benchmarking against offset wells For Production Operations:

ESP failure prediction 30-60 days in advance with specific maintenance recommendations

Gas-lift optimization that automatically adjusts injection rates based on reservoir pressure and fluid properties

Chemical injection programs that adapt to changing water chemistry and scale formation risks

For Asset Management:

Integrated well ranking that combines reservoir potential, equipment health, and economic thresholds

Regulatory compliance monitoring with automated reporting for state commissions

Environmental impact prediction with early warning systems for H2S excursions and emissions events

ROI Through Operational Excellence

Data² customers in upstream O&G typically see:

25-40% reduction in unplanned downtime through predictive maintenance integrated across all equipment types

15-30% improvement in drilling efficiency via real-time parameter optimization with full explainability

10-20% production uplift through continuous artificial lift optimization and reservoir management

50-70% faster regulatory compliance with automated data integration and transparent AI audit trails

Enterprise Grade Security for Critical Infrastructure

Zero-trust architecture ensures Data² meets the highest security standards for critical energy infrastructure:

CMMC 2.0 compliance for DoD contractors

SOC 2 Type II certification for enterprise data protection

Scaling Without a 50-Person Data Science Team

    Low-/no-code MLOps platforms retrain ESP-failure models as reservoir pressure depletes, integrated with Emerson and GE systems.

Auto-generated dashboards turn vibration spectra into “traffic-light” work orders for field techs, pushed directly to mobile devices.

API-first architectures stream model insights into PI or SCADA, plus integration with Schlumberger DELFI platforms, no swivel-chair analytics.

    Federated learning allows operators to pool non-competitive vibration and failure data, boosting model accuracy without exposing proprietary reservoir or production data.

Common Pitfalls, and How Leaders Avoid Them

IT-Only Pilot

Consequence: “Science project” never leaves lab

Fix: Make production, drilling, and HSE metrics the charter; co-own with operations VP and drilling manager

Dirty Historian Data

Consequence: False positives, mistrust from field crews

Fix: Data governance, unit normalization, sensor-health monitoring aligned to API 653/570 standards

Pilot Purgatory

Consequence: Value stalls at one field or drilling rig

Fix: Cloud-native, microservice deployment plus changemanagement for field crews and drilling teams

Opaque Models

Consequence: Texas Railroad Commission or North Dakota Industrial Commission push-back

Fix: Model cards, explainability dashboards, audit trails aligned to API

1160/1175 and state regulatory requirements

Ignoring Field Expertise

Consequence: Models that contradict decades of operational knowledge

Fix: Co-develop with senior production engineers and drilling supervisors; embed domain expertise in feature engineering

Measuring Success in Barrels, BTUs, and Lease Operated Expense Risk

Production deferment avoided (bbl/d or BOE/d) with specific attribution to AI-driven interventions

ESP and rod pump run-life extension (days) with measurable MTBF improvements

Drilling performance (ROP improvement %, stuck pipe incidents avoided, total well cost reduction)

Compression uptime (%) and fuel-gas saved (MMscf) through predictive maintenance

Leak detection MTTD/MTTR (minutes) for regulatory compliance and environmental protection

Reservoir EUR optimization (% improvement) through AI-guided completion and production strategies

Operating cost reductions and lower lease operated expense risk

($/bbl or $/day)

User adoption (% of drillers, production operators, and controllers accepting AI recommendations)

Five-Year Outlook for Upstream & Midstream AI

    Closed-loop drilling optimization: AI autonomously adjusts drilling parameters in real-time, with drillers approving only exception-based interventions.

    Unified production copilots: LLMs fuse ESP data, pressure trends, fluid analysis, and economic models into single optimization recommendations per well.

    Autonomous artificial lift: ML systems automatically switch between gas-lift, ESP, and rod pump operations based on reservoir conditions and economic thresholds.

Real-time emissions trading: AI predicts flare events hours ahead, automatically hedging carbon exposure and optimizing gas capture.

Regulator-grade explainability: Texas Railroad Commission, North Dakota Industrial Commission, and EPA will require transparent ML models for safety-critical drilling and production decisions.

    Data-sharing consortia: Operators pool non-competitive drilling performance and equipment failure data; federated learning boosts model accuracy across unconventional plays.

Bottom Line for Upstream & Midstream Operators

AI is transitioning from ‘nice-to-have’ to ‘must-have’ for optimizing EUR, extending well life, and maintaining HSE compliance in increasingly challenging reservoirs. The differentiator is simple: turn every model, whether it predicts differential sticking or ESP failure, into measurable production gains, cost savings, or reduced lease operated expense.

The operators who master AI-driven drilling efficiency and production optimization today will dominate tomorrow’s unconventional plays and offshore developments. In an industry where a 5% EUR improvement can mean millions in additional revenue per well, AI isn’t just technology, it’s competitive survival.

With Data²’s reView platform, operators don’t just implement AI, they build sustainable competitive advantage through explainable, integrated intelligence that grows smarter with every barrel produced.

Jon Brewton is the Founder and CEO of Data Squared, a pioneering technology company specializing in AI-driven data intelligence. With extensive experience in enterprise data systems and artificial intelligence, Jon leads Data Squared in developing the reView platform, a revolutionary approach to data analysis that combines transparent AI with sophisticated graph technology. Data Squared competes in the all source intelligence and business analytics market alongside companies like Palantir and C3ai, but differentiates through its unique ability to deliver explainable, trustworthy insights through a flexible, cloud-agnostic architecture. The company’s solutions have been successfully implemented across government and enterprise sectors, including all source intelligence and cybersecurity for US government agencies, and with industry leaders in the Oil & Gas sector.

The post AI in Upstream and Midstream Oil and Gas: Lifting Barrels, Lowering Lease Operated Expense appeared first on Energy News Beat.

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