The use of optimization to help the decision process during field development could allow finding, in an automated and robust manner, the best field configuration based on certain requirements or constraints. Several studies exist in the literature that use optimization to improve field development, by finding the optimal configuration of parameters like well characteristics, drilling program, production and injection strategies, etc., to maximize the economic outcomes or the oil/gas recovery. Many of these studies (Jonsbråten 1998; Túpac et al. 2007; Bellout et al. 2012; Simonov et al. 2019) depend on complex reservoir models to represent the production system performance, but in early phases of FDP, complex reservoir models are typically not available. In addition, by using reservoir models only, these studies are not considering the effect of the backpressure of wells and network systems on the sand face when computing production profiles and this can lead to significant errors. On the other hand, other studies (Storvold 2012) used a multiphase flow simulator of the production system to generate data that are later used in the optimization. In such cases, the effect of reservoir decline and depletion on the production system is not captured accurately and this can lead to errors or a poor representation of the production performance. Other authors (Nazarian 2002; Litvak et al. 2007; Volz et al. 2008; Litvak and Angert 2009; Litvak et al. 2011; Silva et al. 2019) have used a more realistic representation of the production system by integrating models of subsurface and surface facilities, improving their capability to represent the performance of the field. Regarding the optimization workflow used by these studies, in general, complex models that are usually difficult to set up and required considerable computational power, require also complex optimization methods that are time-consuming and need considerable time to set up. For example, stochastic mixed-integer problems were used in the work of Jonsbråten (1998), nonlinear optimization in the work of Bellout et al. (2012) and Silva et al. (2019) or genetic algorithms in the works of Nazarian (2002), Túpac et al. (2007) and Litvak et al. (2007).
Accurately estimating costs during early field development is especially difficult since detailed specifications of the production system components are needed and might not be available at this stage. Moreover, if the design conditions are changed, it takes considerable time and effort to compute the updated cost figures. A possible alternative is to generate parametric estimation of costs, which consists of models based on previous projects and historical cost figures. The literature presents several works (Karlik 1991; Jablonowski and Strachan 2008; Kuznetsov et al. 2011; Nunes et al. 2017; Nunes et al. 2018), which used standard regression analysis of cost data to generate equations that fit these data and allow performing predictive estimations of costs.
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