Orocual field is one of the largest growing onshore opportunities in North of Monagas basin, eastern Venezuela. The field is planning to increase its production potential to more than 500% in the next five years. Business plan involve new expansion opportunities with improving field economics. These opportunities include massive development of the shallow heavy oil horizons by steam injection and
development drilling in the deeper light and condensate reservoirs. To accomplish such a challenging goal, it was necessary to estimate new requirements for surface facilities while considering both reservoir uncertainties and multiple development scenarios.
This paper presents a unique and innovated method and a case-study for integrating multiple-reservoir forecasts with a surface facilities network, with economics and uncertainty. Subsurface responses from five Orocual formations were obtained from ten different reservoir simulation models with their associated well constraints. One single surface network model was used to gather production information from all the reservoirs and likewise was used to develop alternate production scenarios. An automated workflow handled the
integration of reservoir production uncertainty, drilling schedule compliance, workover success, economics and varying surface facilities capacities.
The procedure that we have developed in this effort permitted the visualization of a more realistic asset performance compared to requirements in the long-term. The procedure also identified future needs for artificial lift.
The methodology developed also served as a platform for the exhaustive optimization of wellbore and surface equipment sizing in the presence of uncertainties based on front-endloading (FEL) methodology. The procedure allowed the evaluation of parameters that affect uncertainty in well productivity, drilling schedule compliance, workover success, and varying surface facilities capacities, such as project
execution time, workover success, facilities uptime, and facilities spare capacity.
Field production profiles often deviate from simulated ones. Multi-disciplinary field study is traditionally a sequential process; decisions are often broken down and disconnected. Often, reservoir engineers just model reservoir response up to the bottom-hole, production engineers model the whole wellbore up to the well-head, and process engineers model the surface facilities from the wellhead to the tank [Saputelli et al., 2002]. In general, most parties assume constant pressures at the boundaries throughout simulation period.
During field development, not all subsurface uncertainties are considered for evaluating all feasible surface scenarios. Changes in well productivity, water-front advance, free-gas production, and fluid composition will affect both reservoir and surface response. Because of the previous, surface facilities may remain sub-utilized, reservoir potential may not be obtained, and field economics may not be achieved at peak performance.
PDVSA has implemented a planning methodology for selecting the optimal field exploitation strategy called MIAS (sustainable integrated asset modeling) [Acosta et al., 2005; Khan et al., 2006]. MIAS Orocual project's objective is to assure optimal short-term field operating strategies in agreement with long-term reservoir management objectives with social and environmental responsibility. Before the
project began, MIAS Orocual project required the readiness of a platform [Rodriguez et al., 2006] for the quantification of subsurface, wells, and surface uncertainty variables and the evaluation of the effect on the value creation.
An automated workflow for integrating multiple numerical reservoir simulated production profiles within one surface facilities network was developed and is presented in this paper.