Data and Model Integration Challenges for Integrated Production Forecasting-Experience from Super-Giant Onshore Fields

Abstract

To optimize production of a supergiant field, operators require an integrated approach to production forecasting that incorporate subsurface models of multiple reservoirs, well performance, surface equipment and facility constraints. This helps asset managers plan and optimize production on a well-by-well basis to achieve maximum system deliverability. This paper addresses challenges of integrating huge amounts of data, model framework and automated workflows to identify opportunities in debottlenecking, production target sustainability and deliverability.

The integrated production system model involves a simplistic bottom-up approach, in which advanced integration of subsurface and surface elements was facilitated through automated workflows within a digital oilfield system. These automated workflows enable converting multiple reservoir simulation output files to a standard format and mapping of common well names in simulation outputs and in well and facility models. An event-controlled scheduling process in a single working environment enabled analysis of production targets in realistic facility situations for future time steps. The decline in reservoir pressure is modeled, with new well performance in facility model based on changing reservoir conditions.

This automated environment has been adapted to evaluate two giant onshore fields encompassing multiple reservoirs, thousands of wells and numerous distributed-process facilities. The forecasting process confirms deliverability of planned and sustainable production and identifies opportunities for additional field potential, thus facilitating CAPEX optimization for future well costs. The integrated asset forecast was successfully carried out over a five- year timeframe at monthly intervals. One of the most important results obtained from such a forecast simulation was obtaining the maximum feasible rates that can be expected from the asset for each month during the next five years. This collaborative solution has successfully demonstrated the value of data and software integration, and addressed the challenges in integrated production forecasting. The results prove that this is a powerful tool for short- and medium- term forecasting, enabling asset managers to plan field operations, drilling, workovers, and facility improvement projects

This paper describes an integrated production forecasting setup. The case study explains process steps, challenges, simplification and results. The modeling and integration process enables the asset operator to plan remedial subsurface and surface projects to sustain planned rates over time. The forecasting process helps in quick business decision making, thus minimizing uncertainties in deliverability of future production mandates and highlights production-enhancement opportunities.