Large, multiple-field Exploration & Production (E&P) assets require long-term commitments of capital that are tied to decisions on facilities, wells, scheduling, and production strategy. The decisions often must be made when there are high uncertainties, leading to risks. This paper presents a system which integrates finite-difference reservoir simulation, an economics model, and a Monte Carlo algorithm with a global optimization search algorithm to identify more optimal reservoir planning and management decision alternatives under conditions of uncertainty, such that the associated risks are managed. The optimization problem is posed with the business goals stated as a general objective function and includes all constraints (economic, reservoir, production, and statistical) that need to be honored.
The method is illustrated with an example of an E&P asset with multiple oil fields produced through a common surface network. The formulation of the example problem includes decision variables for the scheduling of reservoir units, the number of wells, and production rate capacities. It incorporates the nonlinear response of the objective to reservoir performance and surface pressure constraint through a flow simulator. The analysis is multi-period, evaluating the impact of predicted performance over time for each decision alternative. The individual reservoir units have uncertainties in hydrocarbon volumes and quality, reservoir deliverability, and costs. Decision solutions for objective functions of net present value (NPV) that mitigate risks are presented.
Introduction
Making better decisions, which take into account uncertainty in all the components of an E&P asset's value chain over the time horizon of interest, continues to be a significant challenge for the industry. There are many alternatives in the development of large E&P assets. For example, in a new development of multiple fields there are decisions on numbers and types of wells, numbers and types of platforms, processing facilities, drainage strategies, gas management, scheduling, use of capital, etc. Flow simulation packages give little guidance in identifying good alternatives. An E&P planning problem has such a large number of alternatives that one cannot simply search exhaustively for the best solutions, particularly when uncertainties are present.
The literature of the last few years has emphasized the importance of workflows that integrate and include formal quantification of uncertainties across subsurface, well locations, well configurations and operations, surface interconnections, and economics. However, the industry continues to make many field development decisions based primarily on flow simulation 'sensitivity cases or from simplified models, using simplifying constraints, which can have the effect of underestimating the full uncertainty and yielding much less than the full potential value of the asset.
There has not been a single technology that fully integrates rigorous reservoir modeling, flow simulation, and economics within a decision optimization framework and explicitly manages risk. The problems are highly nonlinear with numerous linear and nonlinear constraints, dependence on time, and multiple local optima. The decision variables can be continuous or discrete, and the number of solution combinations explodes exponentially with the number of discrete variables, which can make common practices such as developing decision trees and running a few case studies virtually useless in guiding engineers to more optimal solutions. Because the difficult computational challenge, much of the previous literature has focused on an individual aspect of the decision optimization process.
Narayanan et al1 presented a case and scenario analysis system for evaluating uncertainties in the value chain of the E&P system, and they framed the problem for uncertain state parameters and decision variables. They used Monte Carlo analysis to span the parameter space of decision alternatives and used a flow simulator for the production response, including multiple well plans and surface networks. Floris et al2 presented a decision scenario analysis framework, focused on scenario and probabilistic analysis also using Monte Carlo, and they suggested proxy models be used for the production response.
