Closing the Production Planning Gap in Africa: A Constraint-Aware Digital Forecasting Framework for Complex Upstream Assets

Abstract

Precise and dependable production forecasting is essential for effective business planning in the upstream oil and gas industry. However, conventional spreadsheet-based workflows invariably falter when confronted with contemporary operational difficulties, such as multidisciplinary data silos, non-integrated business assumptions, and frequent production delays. This results in subjective, non-repeatable, and non-auditable forecasting methods, ultimately causing substantial deviations from anticipated production targets and compromising investment decisions. This paper introduces a contemporary, enterprise-capable resolution to this issue: the SEPAL Business Forecasting Solution (SEPAL BFS) developed by CypherCrescent Limited. SEPAL BFS is a comprehensive, volume-centric, and constraint-sensitive digital platform engineered to emulate the operating reality of hydrocarbon assets. It substitutes isolated spreadsheets with a cohesive system that mandates interdisciplinary data validation, executes automated quality assessments, and adeptly simulates network constraints, including facility limitations, flow assurance challenges, well interdependencies, and consolidated production deferments. This ensures a clear, verifiable, and technically justifiable forecasting process. The approach was implemented across various production assets in the Niger Delta and evaluated against traditional forecasting methodologies. Results indicate a 20–35% decrease in forecast variance over medium-term planning horizons, with forecast update cycle times diminished from several weeks to a few days. The planning uncertainty bands were reduced by as much as 25%, enhancing confidence in production objectives and investment choices. The platform facilitated swift scenario analysis, permitting operators to objectively assess trade-offs between optimising output from high-performing wells and utilising a wider range of well inventory to address facility and commercial limitations. The research illustrates that integrated; constraint-aware digital forecasting markedly enhances prediction credibility, transparency, and auditability. This work's primary contribution is the establishment of a practical, scalable digital forecasting framework that improves production optimisation, enhances decision-making readiness, and offers a robust foundation for revenue forecasting and capital allocation in a complex and dynamic upstream operational context.