Benefits of Real-Time Production Optimization for a Complex Offshore Multi-Field Asset in Brazil

Abstract

Developing Oil & Gas assets requires planning production on multiple horizons: (1) the long-term production plan includes strategic decisions for technology and recovery strategies to maximize the Net Present Value (NPV) of the project, (2) the mid-term horizon includes the drilling program and reservoir depletion/injection rates and (3) the short-term optimization (real-time production optimization or RTPO) aims to maximize the usage of the existing facilities. In the case of RTPO, both subsurface and surface systems are important, the goal being to maximize the daily production while honoring all operational constraints. RTPO requires a comprehensive integrated model covering the entire production system and an accurate mathematical formulation of the problem. This implies finding an appropriate optimization strategy and solver to find an optimal solution within a reasonable time. Sustainable production optimization solutions also assume continuous model update, maintenance and improvement, as the production system behavior changes over time. In this paper, we develop an integrated model for a complex multi-field asset. The production system includes 12 gas wells, 24 gas-lifted oil wells, 4 gas-injection wells, 4 CO2-injection wells, subsea manifolds, gas pipelines, offshore process facilities and CO2 removal units. Gas production from each field is gathered in a single gas pipeline system connected to a gas processing facility located onshore. Control variables include wellhead pressures, routing of wells, gas lift rates, flaring and re-injection rates. Many capacity, pressure and compositional constraints are considered through the whole production system. The production optimization model including binary variables and non-smooth non-linear functions is rather challenging to solve. Each part of the integrated model is approximated with multidimensional piecewise-linear functions to a desired degree of accuracy. The resulting Mixed Integer Linear Program (MILP) can be solved efficiently with existing commercial solvers. The optimization solution is used to answer different types of challenge: (1) platform start-up, (2) unexpected failure of a gas compressor, (3) maintenance on a group of wells and (4) changing reservoir conditions. Production increase driven by RTPO ranges from 1 to 5 % with no additional CAPEX. The implementation of the production optimization solution is also discussed. The importance of the usability, user training and solution maintenance is highlighted.

Short-Term Model-Based Production Optimization for a Gas Field in North Africa

“…Without a decision support tool, a typical decision is taken based on (1) the field data (e.g. flow metering), (2) a limited number of parametric studies launched on simulators and (3) the production engineer experience [3]. A decision support tool leads to a more informed and robust decision-making process and ultimately to higher revenues for the operators, since it can evaluate numerous alternatives that could not be simulated manually.…”

“…The production engineer's experience still has a relative importance in the decision-making process: data validation, uncertainty management, exploring the operating envelope, instability control. Figure 1 shows how a short-term production optimization solution can be integrated in daily production operations: (1) data is acquired from the field, (2) production engineers validate the acquired data (3) models are updated to match field measurements, (4) the optimization algorithm is run iteratively by the operator (the operator can check the optimal solution against more detailed models and re-launch different optimization cases with more refined constraints) and (5) a decision is made and actions are finally taken on the production system [3]. Figure 1.…”