Sample AI use cases for Oil and Gas business

Problem
Managers spend a lot of time searching for specific production data that isn’t covered by standard reports or dashboards. For example, queries like “Why did well #X experience a drop in production in June?” or “Compare the production trends at Field XYZ over the last quarter” require manual data analysis. This leads to delays:
- To answer non-standard questions takes hours or even days.
- Employees are overloaded with routine analytical tasks (data filtering, chart creation).
- Decisions are made slowly due to a lack of timely information.

AI assistant can:

• Accept questions in natural language, clarify details, and quickly find the required production data in the database.
• Build graphs and visual reports in real time based on the retrieved data.
• Perform rapid screening for anomalies over selected time periods, flagging unexpected changes in key indicators.

Expected result

• Faster access to information: responses to ad-hoc inquiries (including visualizations) take only a few minutes not hours.
• Reduced routine analytics by about 70%, as many standard queries are processed automatically.
• Quicker and more confident decision-making thanks to fast analytics delivered directly through the dialogue with the AI assistant.

Problem
The company stores a vast array of reports (in a corporate repository), and employees find it difficult to quickly locate the necessary information. Detailed analysis such as summarizing data from multiple sources, comparing reports or identifying contradictions in documentation - require significant time and effort.

AI assistant can:

• Accept free-form queries, like “What were the prices for ESP pumps in 2023 projects” or “Which documents outline the gaslifted well start-up procedure.”
• Identify specific topics and quickly find relevant information across documents in the repository.
• Detect contradictory or discrepancies between various documents (inconsistency, report errors, etc.).
• Prepare short, clear summary reports or overviews so that employees don’t have to manually click through dozens of files.

Expected result

• Significant acceleration of data retrieval: obtaining results takes just a few minutes instead of many hours.
• Rapid consistency checks: identifying discrepancies and inconsistencies in reports within seconds.
• Increased work efficiency: management and discipline engineers receive ready-made analytics without lengthy manual searches.

Problem
The company often overpays when signing service contracts. This happens due to:
Employees who initiate purchases and procurement specialists don’t always know what constitutes a fair price for services.
There’s no efficient system for quickly comparing current contractor proposals with historical data or market benchmarks.
A tendency to avoid conflict: sometimes employees don't challenge inflated prices because they’re unsure of their calculations or hesitant to engage in negotiations.

AI assistant can:
• Analyze contract histories to automatically identify average prices for similar services from past projects.
• Build a “service-price” library with standardized metrics, allowing price estimates for new, similar services, even when descriptions slightly differ.
• Compare prices in real time by checking contractor proposals against internal company data and market indicators (e.g., regional pricing trends, seasonal fluctuations).
• Provide negotiation support by highlighting key cost items and suggesting arguments for price reductions.
• Recommend ways to reduce uncertainties in scope and specifications, hence reduce the quotation prices.

Expected result
• Reduced overpayments: contracts will be based on objective data rather than subjective judgments.
• Greater confidence for procurement teams: they’ll be able to verify prices quickly and negotiate with solid facts.
• Improved transparency: decisions will be backed by historical data and market analysis, making contract approvals smoother.
• Risk mitigation: the system will help spot subtle pricing discrepancies and reduce reliance on individual opinions.
• Optimized service-related expenses for the company.

Problem
When designing new wells, sites, or upgrading existing facilities, a massive volume of project documentation, drawings, and calculations is generated. Review of those documents are:
- Challenging due to diversity of the docs and need for cross checks (mismatched specifications, non-compliance with standards).
- Time-consuming, prone to human error, and risk overlooking critical issues.
- Increasing the risk of higher costs at later stages due to the need for corrections or rework.

AI assistant can:

• Automatically cross-check parts of the documentation (3D models, specifications, basis for design etc) to identify inconsistencies or gaps.
• Verify compliance with corporate and government standards.
• Provide recommendations for corrections.

Expected result

• Significant reduction in project errors and delays during approval processes.
• Faster project commissioning through quick and accurate document audits.
• Cost savings by preventing project rework and revisions during construction phases.

Problem
There is large volume of data on HSE and industrial safety incidents. Reports, investigation documents, risk assessments, and other records are stored across various platforms (portals, repositories, local files). It’s challenging for HSE specialists or any other responsible parties to quickly find relevant information, identify patterns in recurring incidents, and generate reports for management or regulatory bodies. Manual analysis of these documents is time-consuming and may result in missing critical safety details.

AI assistant can:

• Process free-form queries, e.g., “List common causes of fall-related injuries” or “Show all incidents involving chemical leaks in the past year.”
• Quickly retrieve and summarize data from different sources, categorizing incidents by type, cause, corrective actions, and investigation outcomes.
• Identify recurring issues (e.g., similar causes across different sites) and detect conflicting information in reports and risk assessments.
• Generate clear reports with statistics, diagrams, recommendations, and references to regulatory or internal documents.
• Provide safety improvement suggestions based on identified trends and best practices (e.g., updating procedures, enhancing control measures in specific areas).

Expected result

• Considerable time savings in incident data analysis time: ready-to-use overviews and analytics generated in minutes instead of hours of manual work.
• Quick detection of recurring problems: the chatbot identifies trends in seconds, enabling timely implementation of corrective and preventive actions.
• Improved safety performance: continuous monitoring and centralized data analysis help HSE teams and management respond swiftly to emerging risks.
• Enhanced operational efficiency: less manual effort spent on data processing, freeing up time for decision-making and proactive risk management.

Problem
Equipment operating and maintenance instructions (e.g., for pipeline block valve replacement, instrument calibration instruction) often become outdated, overly generic, or contain errors.
The review and update process is time-consuming and not always thorough.

AI assistant can:

• Compare current procedures with up-to-date internal standards and regulatory documents (e.g., DEP, API),
• Highlighting outdated sections (e.g., “Section 2.4: refers to gasket material - PTFE (Teflon). This is not compliant with API 6A, Section 10.4, which mandates metallic ring-type joint (RTJ) gaskets for pressures above 3,000 psi due to strength and sealing integrity.”)
• Add safety warnings based on past incidents (e.g., “Avoid using tool Z as it caused thread damage in 2022”).
• Generate knowledge-check quizzes for staff if needed.

Expected result

• Shortened procedure update cycle: reduced from months to days.
• Fewer non-conformities during audits: improved compliance with standards.
• Higher work quality: up-to-date, detailed instructions enhance performance and safety.

Problem
Preparing structured reviews (HAZOP, Environmental Impact Assessments, pre-startup reviews) and risk assessments is time-consuming, often taking weeks or months due to manual data collection from scattered sources. Important aspects may be missed, leading to costly project revisions or safety incidents.

AI assistant can:

• Analyze internal or external standards, regulations, and past project reports for insights.
• Reduce subjectivity in risk assessments by suggesting objective metrics based on incident statistics (e.g., “12% of pump failures led to 7+ day downtimes”).
• Verify assumptions against industry norms (e.g., “Failure rate listed as 0.1%, but NORSOK standards for Arctic conditions suggest 0.3%”).
• Generate structured reports highlighting critical risks (e.g., “Top 3 compressor station risks: weld corrosion (18% likelihood), PLC misconfiguration (12%), pressure inconsistencies (9%)”).
• Create adaptive checklists for auditors tailored to project specifics.

Expected result

• Significant reduction in review preparation time: automation handles 50–90% of routine tasks.
• More comprehensive risk identification: cross-discipline data analysis improves risk detection.
• Reduced project delays: fewer unforeseen issues (~10–20 days saved annually per company).

Problem
Project reviewers may miss critical errors due to the large volume of documentation (e.g., mismatched pipe diameters between P&ID diagrams and specifications, incorrect material selection). This can lead to costly late-stage changes, project delays, or even incidents.

AI assistant can:

• Cross-check data across different project sections (e.g., “wellhead pressure in hydraulic analysis vs. valve datasheet”).
• Identify discrepancies (e.g., “Section 3.2 specifies steel grade X, but sour service conditions require grade Y as per NACE MR0175”).
• Prioritize review comments to focus on the most critical issues.

Expected result

• Reduced risk of missing critical errors.
• 20–40% faster project review processes.

Problem
Employees often set vague, non-specific goals, making them hard to measure and track. Some goals are too generic, disconnected from daily tasks, and misaligned with company strategy, which affects motivation, performance, and accountability.

AI assistant can:

• Help employees refine broad goals into SMART objectives (Specific, Measurable, Achievable, Relevant, Time-bound).
• Suggest goal formulations aligned with corporate KPIs and departmental targets.
• Create “roadmaps” for assigned goals, including actionable steps, checkpoints, and metrics.

Expected result

• Improved goal quality: clear, measurable, and motivating objectives that are easier to track and adjust.
• Increased productivity: employees focus on meaningful tasks with a clear understanding of expected outcomes.
• Stronger alignment with corporate strategy: individual and team goals directly support business priorities.
• Faster goal-setting processes: managers can quickly review and approve goals, freeing time for strategic work.

Problem
Capital projects often repeat cost overruns due to insufficient learning from past experiences.
- Valuable lessons and best practices are dispersed across various reports and not systematically analyzed.
- Project teams lack a centralized view of historical insights related to cost drivers.
- The absence of structured knowledge sharing leads to repeated mistakes and missed opportunities for improvement.

AI assistant can:

• "Digest" existing post project reports and After Action Reviews and create structured knowledge base.
• Create chatbot assisting project teams to quickly reveal any relevant learnings or cost benchmarks
• Create voice controlled or free text input assistant which may collect any unstructured and dispersed inputs from project team members to form consistent effective post project analysis report and to update the Knowledge base.

Expected result

• Structured and easy to maintain projects knowledge base with easy (context aware) navigation
• Easy voice or text input of new learnings to keep knowledge base evergreen
• Quick retrieval of the relevant learnings for new project directly helping to avoid mistakes and cost overruns.

Problem
Employees frequently struggle to quickly locate relevant HR policies when facing specific workplace situations or uncertainties, leading to confusion, incorrect actions, or delays in resolving issues. Navigating through extensive documentation or waiting for HR personnel responses can be time-consuming and inefficient.

AI assistant can:

• Understand and respond to natural-language queries, guiding employees directly to relevant HR policies.
• Provide concise, practical guidance on applying HR policies in various scenarios, such as leave applications, workplace conflicts, or benefits inquiries.
• Suggest appropriate next steps or escalation routes depending on the employee's situation.

Expected result

• Faster and more accurate access to HR information, reducing employees’ dependency on HR personnel.
• Improved compliance with HR policies, minimizing misunderstandings and incorrect applications.
• Enhanced employee satisfaction and clarity regarding workplace rules and procedures.
• Reduced workload on HR departments, allowing HR professionals to focus on strategic tasks rather than routine inquiries.

Problem
When defining execution strategies, engineering and corporate-function teams often converge on familiar solutions, missing innovative approaches because:
- Brainstorm sessions rely on the same limited experience pool.
- Time pressure discourages exploring unconventional ideas.
- Potential risks of novel options aren’t quickly quantifiable, so they’re dismissed.

AI assistant can:

• Generate dozens of alternative concepts using cross-industry analogies and TRIZ-style heuristics.
• Cluster ideas by impact, novelty, cost, and implementation horizon.
• Run rapid feasibility checks against company constraints (regulations, assets, budgets).
• Simulate risk profiles and highlight top mitigation levers for each option.
• Provide evidence packs - case studies, precedent data, and expert citations - to support bold choices.
• Track decision history, noting discarded hypotheses and rationales for future learning.

Expected result

• 3-4 × increase in viable options screened per project kickoff.
• Higher innovation rate - >25 % of selected plans classified as “non-obvious yet feasible.”
• Sharper risk awareness; mitigation actions attached to every shortlisted idea.
• Reduced workshop time by 40 % via AI-driven pre-work.
• Stronger stakeholder buy-in thanks to transparent evidence packs.
• Continuous organizational memory of explored ideas, avoiding reinvention.

Problem
Engineering and project teams sit on terabytes of legacy files - field studies, project reports, cost sheets, drilling reports - stored in mixed formats. Because the information is unstructured:
- Analysts spend days manually copying key figures into spreadsheets.
- Benchmark datasets remain incomplete, inconsistent, and quickly outdated.
- Missed insights lead to overbudget estimates, sub-optimal designs, and slow approvals.

AI assistant can:

• Auto-ingest PDFs, spreadsheets, and e-mails, detecting tables, units, and context.
• Normalize disparate terminology (e.g., “lead time” vs “LT”) via domain ontologies.
• Deduplicate near-identical entries and flag contradictory values for review.
• Populate a central benchmark database segmented by project type, well class, geography or other criteria.
• Expose a conversational interface for ad-hoc queries and on-the-fly pivot charts.

Expected result

• >80 % reduction in manual data-entry hours within three months.
• Single source of truth for costs, complexity, and cycle times across projects.
• Faster benchmarking - seconds instead of days to produce analog tables.
• More accurate cost estimates (±5 % variance vs ±20 % today).
• Audit trail of every parsed data point, boosting trust and compliance.
• Reusable dataset accelerates new-field screening and well workover planning.

Problem
Most operators hold long lists of shut-in wells whose integrity, economics, and regulatory status must be reassessed before permanent abandonment. Today:
- Data are scattered across production databases, well integrity logs, drilling reports, and legacy spreadsheets, so engineers spend days hunting for basic facts (depth, tubing status, last pressure test).
- Screening wells against corporate decommissioning criteria (safety, cost, regulatory deadlines etc) is manual and error-prone.
- Drafting full decommissioning “P&A packs” for each candidate - scope of work, cost & schedule estimate, risk register etc - takes weeks of repetitive editing and data re-entry.

AI assistant can:

• Auto-ingest PDFs, spreadsheets, and e-mails, detecting tables, units, and context.
• Harvest and harmonise well data from corporate sources (production historians, WIMS, drilling reports, cost trackers) and fill template fields automatically.
• Score and rank shut-in wells using rule-based and math models that weight integrity risk, cost, proximity to facilities etc.
• Draft decommissioning packs (P&A procedures, HSE checklist, permitting forms) in the approved corporate template, ready for engineer review.
• Highlight missing or conflicting data points and trigger targeted queries to data owners or external systems for resolution.

Expected result

• 50-80 % reduction in engineer hours spent on initial well screening and data gathering.
• Decommissioning pack preparation time shrinks from weeks to <1day per well.
• Transparent audit trail of data sources and scoring logic, increasing confidence for management and regulators alike.

Problem
Across business functions, recurring reports (from internal teams and vendors) vary in quality and completeness. Review rigor depends on the individual reviewer’s experience and available time. As a result:
- Inconsistencies, missing sections, and unit/metadata errors slip through.
- Important anomalies or insights (e.g., out-of-trend readings) are missed.
- Vendor submissions require multiple revision cycles.

examples of such reports: RCA, Inspection, Well integrity test, Corrosion & Chem injection monitoring, Production Allocation & Reconciliation Monthly Statements, Emissions, Flaring, and GHG Inventory Reports and many more which generated regularly.

AI assistant can:

• Run a configurable QA/QC checklist per report type (structure & completeness, unit/tag/date consistency, contradiction checks across sections).
• Verify standards compliance with clause-level cross-checks against corporate specs and API/NACE/ISO, citing exact clauses.
• Compare new submissions to prior periods to ensure trend continuity and explain step-changes.
• Auto-generate a QA summary with findings ranked by severity, suggested corrective text, and a vendor/author to-do list.
• This is not a policeman for report authors, but 24*7 assistant to boost reports quality before submission.

Expected result

• 50-75% reduction in reviewer time per report; faster first-pass acceptance.
• 30-50% fewer vendor resubmission cycles due to clear, actionable gap lists.
• Higher defect detection rate: fewer missed anomalies and contradictions.
• Standardized report quality across sites and contractors; improved compliance readiness.