artificial intelligence

Multi-Agent AI Framework to Reduce FEL-To-FID Cycle Time in Upstream Capital Projects

Abstract Front-End Loading (FEL) is the decisive value-shaping phase of upstream capital projects, yet it remains slow, sequential, and heavily dependent on scarce specialist expertise. This paper defines and validates an artificial-intelligence (AI) enabled, end-to-end capital project delivery framework that reduces c...

Centralized AI-Enabled Energy Performance Management Over Enterprise IIoT Platform

Abstract Energy is one of the largest controllable operating costs and a primary source of greenhouse-gas emissions in upstream oil and gas operations. Yet in most large assets, energy information remains fragmented across electrical meters, supervisory control systems, historians, spreadsheets, and monthly reports. Th...

Petroleum Target Explorer (PTX-AIM): A Rigorously Validated Machine Learning Framework for TOC Prediction in Frontier Basins, a Case Study from the Kolmani River-2 Well, Gongola Basin, Upper Benue Trough, Nigeria

Abstract Objectives/Scope The Kolmani River discovery confirmed a working petroleum system in Nigeria's Gongola Basin (Upper Benue Trough), but too few wells and sampled intervals exist for conventional Total Organic Carbon (TOC) prediction to be reliable. We present PTX-AIM (Petroleum Target Explorer – Artificial Inte...

Autonomous Refinery Intelligence Architecture

Abstract Refinery decisions often require evidence that is distributed across plant historians, maintenance systems, work permits, incident records, management-of-change registers, audit actions, shift logs, engineering documents, and workforce-compliance records. Conventional search or dashboard solutions generally ad...

Physics-Guided AI–Numerical Simulation for Sand Production Prediction and Completion Optimization

Abstract Sand production is a nonlinear, path-dependent, and strongly coupled process controlled by reservoir depletion, effective-stress redistribution, near-wellbore stress concentration, elastoplastic failure, damage accumulation, grain debonding, hydraulic detachment, erosion, and particulate transport. Its occurre...

Identification and Localization of Remaining Mobile Oil in Mature Fields Using Integrated Physics-Based and AI/ML Workflow: A Case Study

Abstract Identification and localization of remaining mobile oil are fundamental components of mature-field reservoir management. This task becomes increasingly challenging when large portfolios contain numerous fields, reservoirs, fault blocks, and producing intervals with varying levels of data completeness and subsu...

From Data to Decision: A Field-Validated AI-Assisted Production Intelligence Framework for Opportunity Identification, Artificial Lift Optimization, and Surveillance in the Indus Basin

Abstract Objectives/Scope The increasing complexity of mature oil, gas, and condensate assets has expanded surveillance data volumes while making rapid identification of high-value production opportunities increasingly difficult. Conventional workflows rely on fragmented datasets, manual interpretation, and reactive as...

Legal Issues and Challenges in Disclosure of Information in Breach of Contract by Artificial Intelligence Service Providers

Retrieval-Assisted Scalar Grounding for Natural-Language Optimization: A Diagnostic Study

Abstract Retrieval and structural checks can make natural-language optimization outputs appear usable even when numerical grounding—assigning textual quantities to model parameters—is incomplete. We examine this diagnostic gap through three questions: fixed-catalog schema identification, the validity of coverage/type r...

Digitalized Holistic Sand Management: Sand Production Prediction Across the Sand Face and Risk Analysis from Sand Face to Surface Facilities from Discovery to Abandonment

Abstract This paper highlights the advancements of Sand Management where machine learning (ML) algorithms and Artificial Intelligence (AI) logics have been incorporated in a digital portal at various levels to digitalize holistic Sand management along with visualization dashboards for seamless sand production and risk...

PENGEMBANGAN COURSE TERINTEGRASI AI BERBASIS PROBLEM BASED LEARNING UNTUK MENINGKATKAN KEMAMPUAN PENERAPAN PROSEDUR DAN PEMECAHAN MASALAH SISWA SMP

ABSTRACT Informatics education requires instructional designs that connect conceptual understanding with procedural application and contextual problem-solving. This study aimed to develop an Artificial Intelligence (AI)-integrated course based on Problem-Based Learning (PBL) to support these two competencies among juni...

Driving Trusted Subsurface Data for AI at Scale: Enforcing Data Quality Thresholds and Governance in ADNOC's Data Foundation and ENERGYai

Abstract As subsurface data platforms increasingly power artificial intelligence (AI), machine learning (ML), and agentic workflows, advisory or post-hoc quality control (QC) is insufficient to prevent non-compliant data from propagating at scale. This paper presents ADNOC's enforceable governance and quality framework...