Integrated Modeling of the El Furrial Field Asset Applying Risk and Uncertainty Analysis for the Decision Making (SPE94093)
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Cited by 14 publications
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fax 01-972-952-9435. AbstractOrocual 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. SPE 107259uncertainty, drilling schedule compliance, workover success, and varying surface facilities capacities.In general, the automated workflow, developed in this effort, permitted the visualization of a more realistic asset performance in the long-term identification of future requirements for artificial lift. The methodology developed also serves as a platform for the exhaustive optimization of wellbore and surface equipment sizing in the presence of uncertainties.
fax 01-972-952-9435. AbstractOrocual 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. SPE 107259uncertainty, drilling schedule compliance, workover success, and varying surface facilities capacities.In general, the automated workflow, developed in this effort, permitted the visualization of a more realistic asset performance in the long-term identification of future requirements for artificial lift. The methodology developed also serves as a platform for the exhaustive optimization of wellbore and surface equipment sizing in the presence of uncertainties.
The high oil prices of the last five years have motivated operators to unveil less historically attractive hydrocarbon resources and to optimize production operations from existing fields. In fact, many operators of brown and mature fields around the globe plan increases in production of more than 100% during the next five years. Optimizing production operations may — with the minimum effort— involve identifying opportunities for production increases from reservoirs, from individual wells, and from surface equipment. In addition, to sustain lean operations, production operations will require constant review of processes, management systems, adaptation of people, and applications of smart technology. Often, decisions must be made despite uncertainties of well performance, subsurface response, equipment failure rates, and downstream demands. The heterogeneity of information and the complexity of current assets imply an iterative approach to identify viable opportunities. Opportunities require management of risk and uncertainty. To accomplish such a challenging goal, it is necessary to estimate new capital investments for wells and facilities while considering both reservoir uncertainties and multiple scenarios for development. New capital investments are intended to increase the economic value of daily production operations. We present a structured methodology for integrating reservoir forecasts with wells and with surface facilities and operational issues. We also present several case-studies. Operational issues include well performance, subsurface response, equipment failure rate, and downstream demands. The methodology considers the integration of reservoir production uncertainty, well performance, drilling schedule compliance, workover success, and varying surface facility variables, such as availability, uptime, and capacities. Introduction In the past, multiple oilfield disciplines have been treated and considered as silos. We present a rationale for considering an integrated effort and for considering production operations as the common interface for all disciplines. Current Integration Challenges. The effort to successfully integrate data and processes in the value chain for oilfield production operations has many challenges. By viewing these challenges as sources of uncertainty, we can begin to understand their impact on decisions to effectively optimize production and forecast production and reserves. The widespread risk involved here is that people and systems are not following or supporting optimal processes that will deliver critical data for integration. People/Organizational Challenges. There are a variety of people and organizational challenges. Engineering and operations personnel are increasingly absorbed with day to day activities. The pressure to collect data and deliver daily reports tends to intensify compartmentalized behavior and to discourage information flow between key processes that should be better integrated. As an example, data on the availability of injection gas from compression facilities may not be integrated with the anticipated needs and performance of injection and producing wells and with the characterization of a reservoir. Work force demographics and dynamics also present challenges to integration. As an example, entire asset teams with their own processes and tools frequently merge with a new, parent operating company. The operating company must then assimilate these teams into their organizational structure via a cumbersome balance of centralized and distributed authority — an assimilation process that is further aggravated by asset-level, profit-loss accountability and authority. One of the key pieces of data from recent studies indicates that the average experience level for operations, production and reservoir engineering resources is about 15–20 years. These statistics are consistent with the "Big Crew Change" phenomenon that the oil & gas industry has anticipated. Unfortunately, a large percentage of expert professionals will soon retire, and their companies have not yet been fully able to institutionalize or transfer their knowledge. This perceived shortcoming also exemplifies the challenge of people/organization difficulties.
Field development decisions must be made despite uncertainties of well performance, subsurface response, equipment failure rate, and downstream demands. The heterogeneity of information and complexity of current hydrocarbon assets implies an iterative approach to identify opportunities, which requires risk and uncertainty management. To accomplish such a challenging goal, it is necessary to estimate requirements for surface facilities while considering both reservoir uncertainties and multiple development scenarios, and their link to the economic model. Optimizing production operations may involve identifying opportunities for production increase from reservoir, wells, and surface equipment with the minimum effort. In addition, to make robust planning This paper presents methodologies, technology applications and case-studies for managing risk and uncertainties in the visualization of production scenarios. They are shown examples of multiple reservoir forecasts with wells and surface facilities network models and operational issues, such as well performance, subsurface response, equipment failure rate, and downstream demands. We show several integration procedures for handling reservoir production uncertainty, well performance, drilling schedule compliance, workover success, and varying surface facility variables, such as availability, uptime, and capacities. The examples shown in this paper permits the visualization of a more realistic short-term asset performance while minimizing requirements in the long-term, minimizing risk and manging uncertainties. Introduction Oil and gas projects are affected by technical, economical, political or environmental uncertainties in various remarkable ways [Saputelli et al., 2002]. These uncertainties, which are often difficult to evaluate, affect the ability to make adequate decisions. Frequently, project teams are faced with several misconceived practices:(1)project outcome is evaluated deterministically or in comfortable ranges of uncertainties(2)uncertainties that cannot be evaluated in available models are just ignored and(3)uncertainties are often evaluated by isolated disciplines and not all uncertainties are tested against the overall project results. In despite of the above mentioned, field development decisions must be made. Decisions making under uncertainty must encompass a structure methodology for establishing the model, defining uncertainty and decision variables, propagating uncertainties and(4)analyzing the results. A production scenario involve decision making with respect to reservoir exploitation schema, well planning, well architecture, reservoir interface, lift equipment and surface facilities. The goal of a single, evolving, life-cycle model for oil and gas assets has many benefits for effective and efficient field development and exploitation. However, the size and complexity of the reservoir models often require characterization at several resolutions, thus ranging from full field strategic models to short range operational models. Full field strategic models can be used to evaluate various production scenarios and development strategies and to estimate future drilling and facilities requirements. Short range operational models concentrate on issues such as rate requirements, production decline analysis, etc. Visualization of Production Scenarios Elements of production scenarios Production scenarios are the resulting combinations of options for each of the specific decision categories with respect to reservoir exploitation schema, well planning, well architecture, reservoir interface, lift equipment and surface facilities. These combinations may be the result of the exhaustive investigation of all the possible combination of a large number of permutations. Related to reservoir exploitation: depletion rate, secondary and tertiary recovery scheme (fluid to inject); production and injection allocation for optimum secondary recovery (Ramirez, 1978; Saputelli et al., 2005) Related to well planning: number of active wells, number wells to drill (Cullick et al., 2003; Narayanan et al., 2003; Solis et al., 2004), surface locations, subsurface intervals to produce from, and schedule.
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