Tela? agentic-AI assistant catalog

Innovation Factori? Data

Tela? agentic-AI assistant for unstructured data insights

Automated extraction, classification, and querying of oil and gas unstructured data across OSDU document categories.

Overview

Unstructured Data Insights combines three AI-powered workflows—oil and gas document classification, PVT report extraction, and document question-and-answering—to automate the management of unstructured technical data. By transforming documents into structured, searchable, and traceable information, the solution replaces manual data handling with a single auditable workflow, enabling faster access to trusted insights.

Challenges

Critical technical information is often scattered across multiple repositories, drives, and document stores.
Large volumes of unstructured data make it difficult for engineers to locate and use relevant information efficiently.
Manual document classification and data extraction are time-consuming and prone to errors.
Valuable technical knowledge and intellectual property often remain underutilized.
Lack of traceability can create auditability and governance challenges.

Solution

Automatically organize and classify documents using OSDU-aligned taxonomies.
Extract structured information from complex technical reports and engineering documents.
Enable natural language access to enterprise document repositories.
Maintain traceability from extracted data back to source documents.
Transform unstructured content into trusted, reusable digital assets.

Tela catalog

Features

Oil and Gas Document Classification – Automatically classify unstructured oil and gas documents according to all 13 OSDU-standard document categories. Transform disorganized document archives into structured, searchable repositories while eliminating manual classification efforts.
PVT Report Extraction – Extract fluid properties, test conditions, and measurement values from laboratory PVT reports. Structure extracted data into analysis-ready formats with full traceability to source pages and bounding boxes for direct use in reservoir modeling and simulation workflows.
Document Question and Answering – Enable engineers to query large document repositories using natural language and receive precise, traceable answers sourced directly from relevant documents. Reduce the time required to locate and synthesize technical information.

Benefits

Automated OSDU Classification – Classify documents across all 13 OSDU-standard document categories with full coverage.
End-to-End Traceability – Trace every extracted value back to its source document UUID, page number, and bounding box for complete auditability.
Accelerated Data Processing – Replace days of manual document review and data entry with a single automated workflow.
Improved Data Accessibility – Make unstructured technical information searchable, accessible, and analysis-ready.
Unlock Hidden Value – Reduce operational risk and maximize the value of previously inaccessible technical knowledge and intellectual property.

Innovation Factori? Subsurface

Tela? agentic-AI assistant for geological analog search

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Screen massive 3D seismic volumes for high-potential prospects in a fraction of the time.

Overview

Geological analog search leverages a pretrained seismic foundation model to identify and rank geological analogs across large seismic datasets. By comparing known prospects or proxy models against extensive 3D seismic volumes, the solution enables geoscientists to rapidly discover similar subsurface features, accelerating exploration screening and prospect evaluation.

Challenges

Identifying prospective oil and gas traps within large 3D seismic volumes can be time-consuming.
Geoscientists often rely on finding geological analogs with characteristics similar to proven prospects.
Screening seismic datasets manually can take weeks or months of interpretation effort.
Large data volumes make it difficult to consistently evaluate all potential opportunities.
Manual analog selection can introduce bias and variability into prospect evaluation.

Solution

Leverage a pretrained seismic foundation model that understands seismic patterns and geological features.
Accept either a proxy model of a successful analog or an example prospect as a reference target.
Search across large seismic volumes to identify similar geological features automatically.
Generate probability-ranked analog candidates for further interpretation and evaluation.
Deliver results in minutes rather than weeks, accelerating exploration workflows.

Features

Cross-Survey Heatmap Generation – Generate probability heatmaps across multiple seismic surveys simultaneously, enabling direct comparison of analog quality across datasets. Color-coded visualizations help geoscientists quickly identify areas with the highest similarity to the reference target.
Ranked Analog Results with Geographic Context – Return probability-ranked analog candidates in a searchable table and geographic map view. Users can evaluate similarity scores, locations, and spatial context to rapidly prioritize exploration opportunities.
Petrel Integration and 3D Visualization – Import identified analogs and heatmaps directly into Petrel for 3D visualization and interpretation. Move seamlessly from regional screening to detailed structural and stratigraphic analysis within existing workflows.

Benefits

Accelerate Exploration Screening – Reduce analog hunting from weeks or months to minutes using AI-driven similarity analysis.
Improve Consistency and Objectivity – Apply repeatable, model-driven analog identification across large datasets.
Reduce Exploration Uncertainty – Support prospect evaluation through consistent analog ranking and comparison.
Scale Across Large Seismic Volumes – Screen significantly larger datasets than would be practical through manual interpretation.
Integrate Seamlessly into Existing Workflows – Leverage direct Petrel integration for rapid transition from screening to interpretation.

Innovation Factori? Subsurface

Tela? agentic-AI assistant for smart basin: Agentic AI for stranded gas assets tie-back

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Turn stranded gas opportunities into development-ready insights.

Overview

Smart basin is an agentic AI advisory system that accelerates screening of stranded gas assets for tie-back and development opportunities. By coordinating specialized AI agents across geological, reservoir, drilling, fluid, and compliance domains, the solution compresses months of manual screening into days—surfacing grounded, evidence-backed insights while keeping engineers in control.

Challenges

Screening stranded assets for tie-back potential is a complex, multidisciplinary process involving geological, reservoir, drilling, and facilities evaluations.
Critical technical information is fragmented across thousands of well reports, PVT analyses, field studies, and operational datasets.
Manually synthesizing large volumes of structured and unstructured data is time-consuming and difficult to scale.
Regulatory and environmental compliance assessments require reviewing extensive policy and Environment Plan documentation.
Slow evaluation cycles can delay investment decisions and increase opportunity costs.

Solution

Coordinate specialized AI agents across asset, well, fluid, subsurface, and compliance domains.
Ingest structured and unstructured data into a unified knowledge graph and vector database.
Automatically identify tie-back opportunities, technical constraints, and development considerations.
Route user questions to the most relevant domain experts through agentic orchestration.
Deliver evidence-backed recommendations with traceable citations and supporting documentation.

Tela catalog

Features

Asset Agent – Proximity and Tie-Back Screening – Evaluate well and field proximity, tie-back distance, and hub connectivity using structured spatial data. Support configurable screening criteria across thousands of assets and infrastructure components.
Domain Agents – Well, PVT, and Subsurface Intelligence – Extract and analyze technical information from well reports, PVT studies, and subsurface data, including reservoir conditions, fluid composition, contaminants, and completion details.
Policy and Compliance Agent – Identify regulatory requirements and environmental considerations by analyzing policy documents and approved Environment Plans, providing traceable compliance guidance.
Orchestrator and Multi-Agent Reasoning – Coordinate multiple specialized agents, manage workflow execution, and synthesize outputs into a single, coherent assessment with supporting evidence and citations.

Benefits

Accelerate Asset Screening – Compress multi-month stranded asset evaluations into days.
Improve Decision Confidence – Deliver grounded recommendations backed by source documents, citations, and traceable evidence.
Unify Multi-Domain Analysis – Evaluate asset proximity, subsurface characteristics, fluid properties, and compliance requirements within a single workflow.
Keep Engineers in Control – Augment engineering expertise with AI-driven analysis while preserving human judgment and decision-making.
Scale Across Basins and Portfolios – Adapt to different regions, asset types, and development opportunities through flexible data ingestion and agent orchestration.

Innovation Factori? Drilling

Tela? agentic-AI assistant for offset wells insights

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Design wells faster and smarter with instant, contextual insights.

Overview

Offset wells Insights leverages domain-specific generative AI to transform structured and unstructured offset-well data into actionable drilling intelligence. By consolidating information from reports, databases, and operational records, the solution enables drilling engineers to rapidly identify risks, uncover lessons learned, and apply proven insights to future well designs.

Challenges

Offset well analysis is critical for identifying drilling risks, improving safety, and optimizing well design.
Relevant information is often spread across multiple reports, databases, folders, PDFs, and images.
Data formats are inconsistent and difficult to analyze efficiently.
Engineers spend significant time manually reviewing offset well information.
Manual workflows can delay planning cycles and limit the quality of insights.

Solution

Consolidate structured and unstructured offset-well data into a unified source of intelligence.
Extract and contextualize insights from drilling reports, end-of-well reports, service company reports, and operational records.
Enable engineers to query and interpret offset-well knowledge using natural language.
Surface drilling risks, trends, and lessons learned through domain-specific AI reasoning.
Support faster, more informed well design and planning decisions.

Tela catalog

Features

Drilling Risk Analysis Dashboard – >Synthesize offset-well information into a drilling risk dashboard that categorizes non-productive time (NPT) events by risk type, visualizes risk distribution across wells, and maps risk occurrences geographically.
NPT Breakdown and Timeline Analysis – Analyze non-productive time by activity category and drilling phase, helping teams identify where operational inefficiencies occur and prioritize mitigation strategies.
Contextual Offset Well Intelligence – Enable engineers to search, query, and interpret offset-well data from multiple structured and unstructured sources using natural language interaction.

Benefits

Accelerate Offset Well Analysis – Reduce offset-well review and planning cycles by rapidly extracting insights, trends, and anomalies from historical drilling data.
Improve Planning Accuracy – Leverage drilling-specific intelligence and contextual knowledge to support better-informed well designs.
Reduce Drilling Risk – Increase confidence in identifying and mitigating drilling hazards, well integrity risks, and sources of non-productive time.
Unlock Hidden Knowledge – Transform fragmented reports and operational records into accessible, reusable engineering knowledge.
Enhance Operational Performance – Apply lessons learned from offset wells to improve safety, efficiency, and overall drilling outcomes.

Innovation Factori? Drilling

Tela? agentic-AI assistant for well schematics advisor

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Agentic AI assistant for extracting, validating, and visualizing well schematics and barrier information.

Overview

Well schematic advisor ingests decades of structured and unstructured well documentation to generate contextualized well schematic representations. Designed to support well integrity and plug-and-abandonment (P&A) workflows, it reduces manual review effort, improves risk assessment accuracy, and integrates with well integrity solutions for validation, reporting, and decision support.

Challenges

Well integrity and P&A assessments depend on accurate understanding of historical well conditions, interventions, and barrier status.
Critical information is often buried within decades of unstructured and inconsistent well documentation.
Manual review of legacy reports is time-intensive and resource-intensive.
Key well integrity information can be overlooked due to fragmented data sources and document complexity.
Incomplete understanding of well history can increase operational risk and impact P&A decision quality.

Solution

Ingest and analyze decades of structured and unstructured well documentation.
Generate contextualized well schematics and barrier representations automatically.
Extract well construction, integrity, and barrier information for validation and assessment.
Enable natural language interaction to explore well history and documentation.
Support well integrity and P&A workflows with traceable visualizations, reporting, and decision support.

Tela catalog

Features

Automated Well Schematic Generation – Extract well construction details from historical documentation and automatically generate structured well schematics and wellbore representations for review and analysis.
Well Integrity Data Extraction and Validation – Capture key well properties, barrier information, and wellbore history from reports and documentation, supporting validation workflows and integration with Wellbarrier solutions.
Conversational Well Intelligence – Use generative AI, retrieval-augmented generation (RAG), and drilling-domain expertise to query historical well information through natural language and receive contextualized answers.
Modular Agentic Architecture – Leverage reusable AI agents that can operate independently or through orchestrated workflows, enabling flexible support for multiple user roles and use cases.

Benefits

Accelerate Well Documentation Review – Reduce manual interpretation effort from days to hours.
Support Well Integrity and P&A Decisions – Provide consistent, traceable schematic representations for engineering assessments.
Improve Data Quality and Confidence – Identify inconsistencies, anomalies, and missing information across historical well documentation.
Enhance Communication and Reporting – Deliver visualizations and reports that clearly communicate well barrier status and performance.
Integrate with Existing Engineering Workflows – Leverage standard APIs and interoperability with existing well engineering environments.

Innovation Factori? Production

Tela? agentic-AI assistant for Pipesim? steady-state multiphase flow simulator advisor

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Empower every engineer with the full potential of the Pipesim simulation.

Overview

Tela in Pipesim steady-state multiphase flow simulator helps engineers interact with simulation models through natural language, making advanced production modeling and analysis more accessible. By combining AI-powered assistance with the capabilities of the Pipesim simulator, engineers can run simulations, evaluate production scenarios, identify bottlenecks, and optimize performance with greater speed and confidence.

Challenges

Manual production modeling can be time-consuming and resource-intensive.
Effective use of simulation tools often requires specialized technical expertise.
Complex workflows and siloed systems can slow decision-making.
Engineers may not fully utilize the capabilities of the Pipesim simulator.
Evaluating production optimization scenarios can require significant effort and experience.

Solution

Provide a natural language interface for interacting with Pipesim simulations.
Enable engineers to run simulations, generate plots, and analyze results through conversation.
Help users identify production bottlenecks and evaluate optimization opportunities.
Support workflow automation and reporting through AI-assisted capabilities.
Deliver contextual guidance to help engineers leverage the full capabilities of Pipesim.

Tela catalog

Features

Natural Language Simulation Interface – Run simulations, generate plots, and automate reporting through an intuitive conversational experience.
PTK AI Assistant – Build and execute Python-based workflows using the Python Toolkit (PTK), knowledge graphs, and embedded files.
Contextual Guidance and Recommendations – Receive intelligent assistance, personalized recommendations, and workflow guidance based on the current engineering context.

Benefits

Ask, Simulate, and Optimize – Interact with simulations, evaluate scenarios, and optimize production through a single conversational workflow.
Reduce Manual Effort – Lower the barrier to advanced simulation capabilities and minimize repetitive engineering tasks.
Accelerate Decision-Making – Improve consistency, enhance accuracy, and reach decisions faster.
Enable Collaborative Modeling – Support production modeling and analysis across asset and operational teams.
Unlock Simulation Expertise – Help engineers maximize the value of Pipesim regardless of their experience level.

Innovation Factori? Production

Tela? agentic-AI assistant for production analytics advisor

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Agent-driven solution for automated production engineering workflows.

Overview

Production analytics advisor helps teams maximize the value of their production data faster and with greater confidence. By replacing complex interfaces with natural-language interaction, it enables global teams to seamlessly query data and receive contextual answers. Beyond simple Q&A, the solution executes production engineering workflows such as production forecasting, waterflood surveillance, Chan analysis, and type well analysis to identify opportunities for production improvement without disrupting established workflows.

Challenges

Production data is often stored across multiple trusted systems, databases, and applications.
Extracting insights requires familiarity with complex interfaces, data schemas, and predefined workflows.
Engineers spend significant time navigating tools rather than interpreting results and making decisions.
Production data is available and trusted but not easily accessible to a broader range of users.
Manual execution of production engineering workflows can slow analysis and decision-making.

Solution

Connect directly to existing production data sources, models, and calculations without replacing validated systems.
Leverage established data quality controls and workflows while improving accessibility and usability.
Enable natural-language interaction for querying production data and engineering insights.
Allow users to ask questions in their preferred language and receive contextual answers grounded in trusted production data.
Automate production engineering workflows, including forecasting, waterflood surveillance, Chan analysis, heterogeneity index analysis, and type well analysis.
Identify production improvement opportunities faster through agent-driven analytics and automation.

Tela catalog

Features

Well Ranking and Production Data Query – Enable engineers to rank wells and rapidly identify top-performing and underperforming assets through natural-language queries.
Historical Production Trend Analysis – Retrieve and visualize historical production performance through time-series charts, with AI-generated explanations of trends, anomalies, and potential areas for investigation.
Decline-Curve Analysis – Execute Arps-based decline-curve analysis, generate production forecasts, and allow engineers to adjust parameters and refine forecasts through conversational interaction.

Benefits

Deliver Insights in Seconds – Access production intelligence quickly through natural-language interaction.
Accelerate Decision-Making – Automate production engineering workflows such as forecasting, waterflood surveillance, and production diagnostics.
Reduce Manual Effort – Minimize time spent navigating tools, preparing data, and executing repetitive analyses.
Expand Access to Expertise – Enable a broader set of users to leverage trusted production data without specialized system knowledge.
Focus on High-Value Decisions – Allow engineers to spend more time interpreting results and optimizing production performance.

Innovation Factori? Production

Tela? agentic-AI assistant for asset evaluation insights

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Agentic-AI solution for rapid screening and evaluation of complex subsurface asset portfolios.

Overview

Asset evaluation insights uses multi-agent reasoning to extract, classify, and analyze large volumes of structured and unstructured asset data, including technical reports, well logs, time series, and models, to support acquisition, farm-in, and licensing decisions. Built on the Lumi™ data and AI platform and delivered through Tela™ agentic-AI assistant, it creates structured knowledge repositories and enables natural-language queries that surface risks, opportunities, and ranked asset potential, significantly reducing data wrangling and decision latency.

Challenges

Asset evaluations often involve more than 100 GB of structured and unstructured technical data.
Critical information is distributed across reports, well logs, models, schematics, and time-series datasets.
Experts can spend more than 60% of their time on data gathering, preparation, and interpretation.
Manual data wrangling delays investment decisions and increases opportunity costs.
Evaluating multiple assets consistently and at scale can be difficult and resource-intensive.

Solution

Ingest and organize structured and unstructured asset data into analysis-ready knowledge repositories.
Automatically classify and extract production, reservoir, and technical information from diverse data sources.
Apply multi-agent reasoning to connect, analyze, and interpret asset data.
Enable natural-language interaction for rapid exploration of risks, opportunities, and asset potential.
Accelerate acquisition, farm-in, and licensing evaluations through AI-driven insights and automation.

Tela catalog

Features

Comprehensive Data Ingestion – Ingest a wide range of asset data sources, including technical reports, well logs, schematics, time-series data, and subsurface models.
Multi-Agent Knowledge Intelligence – Leverage specialized AI agents to classify, extract, link, and reason across asset information, creating structured knowledge repositories and traceable insights.
Production and Subsurface Analytics – Analyze production performance, field characteristics, reservoir information, and asset metrics through structured dashboards and analytical workflows.
Natural-Language Asset Evaluation – Query asset data using conversational prompts and receive contextual, evidence-based insights grounded in technical and operational data.
Secure and Governed Deployment – Support human-in-the-loop validation, auditability, and deployment across hybrid, cloud, and on-premises environments.

Benefits

Accelerate Asset Evaluation – Convert large volumes of technical data into actionable insights in a fraction of the time required by manual workflows.
Reduce Manual Effort – Minimize data extraction, preparation, and interpretation across all asset data types.
Improve Decision Quality – Surface contextual, data-driven insights to support acquisition, farm-in, and licensing decisions.
Scale Across Asset Portfolios – Evaluate multiple assets consistently across different basins, environments, and portfolio sizes.
Focus on High-Value Opportunities – Enable experts to spend more time assessing risks and opportunities rather than managing data.

Innovation Factori? Production

Tela? agentic-AI assistant for standard operating procedure compliance advisor

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Real-time AI solution for monitoring operational compliance with safety and operating procedures.

Overview

The standard operating procedure (SOP) compliance advisor uses vision-language models, sensor data, and agentic reasoning to monitor crew actions and operational execution in real time. It detects non-compliance events, flags safety risks, and generates actionable reports aligned with the latest SOP standards, supporting proactive safety management and regulatory compliance.

Challenges

Monitoring compliance with safety and operating procedures is often manual, reactive, and resource-intensive.
Operational activities must be evaluated against evolving site-specific procedures and regulatory requirements.
Traditional monitoring approaches can struggle to detect procedural deviations consistently and at scale.
Limited visibility into compliance trends and recurring violations can hinder continuous improvement efforts.
Delayed detection of non-compliance can increase operational and safety risks.

Solution

Continuously monitor operational activities using live video streams, sensor data, and SOP documentation.
Apply vision-language models to interpret activities and evaluate compliance against current procedures.
Detect non-compliance events in real time and provide contextual alerts and recommendations.
Generate traceable event logs, reports, and compliance insights for audits and investigations.
Enable proactive safety management through continuous monitoring and trend analysis.

Tela catalog

Features

Multi-Modal Compliance Monitoring – Integrate live video streams, sensor data, and SOP documentation to provide comprehensive operational compliance monitoring.
AI-Powered Activity Interpretation – Leverage vision-language models and agentic reasoning to understand operational activities, compare them against SOP requirements, and identify deviations.
Traceable Event Intelligence – Capture detailed event records including observed actions, SOP references, compliance status, participants, and confidence scores to support auditability and reporting.
Natural Language Investigation – Query compliance events, violations, and historical trends using natural language and receive contextual explanations and recommended actions.
Coaching and Hazard Prevention – Identify unsafe behaviors and procedural deviations early, enabling targeted coaching, hazard prevention, and continuous improvement.

Benefits

Detect Non-Compliance in Real Time – Monitor operations continuously and identify deviations as they occur.
Accelerate Safety Response – Leverage edge-based processing for low-latency event detection and alerting.
Improve Compliance Reporting – Automatically generate detailed, actionable reports aligned with site-specific procedures.
Adapt to Changing Standards – Stay aligned with the latest SOP updates and regulatory requirements without manual reconfiguration.
Enhance Operational Safety – Reduce risk through proactive monitoring, coaching, and early intervention.

Innovation Factori? Sustainability

Tela? agentic-AI assistant for CO? post-injection monitoring advisor

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AI-driven seismic monitoring solution for tracking injected CO? plume behavior and containment risks.

Overview

CO? post-injection monitoring advisor leverages a seismic foundation model and agentic orchestration to automate interpretation of time-lapse seismic data. It accelerates delineation of CO? plume boundaries, monitors proximity to faults, wells, and license limits, and supports regulatory compliance through faster, more consistent post-injection analysis compared with manual workflows.

Challenges

Accurate tracking of injected CO? is critical to ensure long-term storage integrity and containment.
Monitoring plume movement and proximity to subsurface boundaries requires extensive seismic interpretation.
Traditional workflows rely heavily on manual analysis, making evaluations time-consuming and resource-intensive.
Manual interpretation can introduce variability and limit the speed of decision-making.
Regulatory compliance requires continuous monitoring and clear evidence of containment performance.

Solution

Automate interpretation of time-lapse seismic data using a seismic foundation model.
Accelerate delineation of CO? plume boundaries and movement over time.
Evaluate proximity to faults, wells, horizons, and license boundaries.
Enable faster, more cost-effective monitoring through agent-driven workflows.
Support regulatory compliance with consistent, traceable post-injection analysis.

Features

Agentic Workflow Orchestration – Leverage orchestrator and ingestion agents to automatically translate user requests into end-to-end workflows, while validating, harmonizing, and preparing data for analysis.
Seismic Foundation Model Intelligence – Apply foundation-model-based interpretation to identify CO? plume boundaries, detect 4D seismic anomalies, and evaluate containment risks with minimal training data.
Conversational Seismic Analysis – Interact with seismic data through natural language to perform survey exploration, anomaly detection, similarity searches, and automated interpretation workflows.
Integrated Monitoring Workflows – Combine seismic interpretation, proximity analysis, geobody generation, and containment assessment into a unified monitoring workflow.

Benefits

Increase Confidence in CO? Containment – Continuously monitor plume behavior and proximity to critical subsurface boundaries.
Accelerate Seismic Interpretation – Reduce analysis time through automated evaluation of 4D seismic data.
Improve Interpretation Consistency – Enhance understanding of plume evolution while reducing variability associated with manual workflows.
Support Regulatory Compliance – Generate traceable monitoring outputs, proximity alerts, and containment assessments.
Enable Better Decision-Making – Visualize plume extent and containment risks to support informed operational and regulatory decisions.

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