Developing an exploitation plan for an oil field has always been challenging. This paper presents different approaches and addresses the important question of how to allocate asset resources to maximize profit. This study presents the analysis of integrated field case studies. Because of their complexities, integrated studies can offer valuable insights. Based on a field's heterogeneity and complexity, it can be relevant to divide it into different investment units. Individual studies can be generated, and based on their results, different exploitation strategies can be visualized. The strategies are based on economic criteria, such as net present value and the efficient use of resources. Capital investment efficiency is the key indicator that should direct resource allocation. Although there is no fixed method to find optimal decisions in a capital project, the FEL (front-end-loading) methodology measures and increases the level of project definition, thereby increasing the probability of project success at any stage of the life of the oil field. Traditionally, economic resources have been the principal metrics for analysis. However, the term resources encompasses many potential inputs to the system, e.g. drilling, facilities, enhanced recovery, and allocations. In complex oil fields with many independent reservoirs, it has been found that each reservoir has its own characteristics. That leaves room to experiment on new combinations for development, such as infill drilling, injection in the most favorable reservoirs or new potentional drilling locations. In many cases, it was found that the oil price had the greatest impact on projects, because their profit margin is lower than the rest of field. Small reservoirs or reservoirs that require marginal investment show the highest efficiency in investment, which means that investment efficiency is inversely related to the total amount of investment. This finding indicates that focusing solely on investment efficiency can mislead the optimal decision. Introduction An integrated field study is traditionally a sequential process; decisions are often broken down and disconnected. Often, reservoir engineers just model reservoir response to the bottom-hole, production engineers model the whole wellbore 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 the simulation period. For the above reasons, project results often deviate from the project plan. In the building of field development plans, not all subsurface uncertainties are considered when 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 these fluctuations, surface facilities may remain sub-utilized, a reservoir's full potential may not be obtained, and field economics may not reach peak performance. Field development decisions must be made despite uncertainties in well performance, subsurface response, equipment failure rate, and downstream demands. The heterogeneity of information and complexity of current hydrocarbon assets requires an iterative approach to identify the best opportunities. To succeed, this approach requires risk and uncertainty management. Optimal field development planning may involve identifying opportunities for increasing production from reservoirs, wells, and surface equipment, with the minimum effort.
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.
This paper describes a comprehensive methodology to rank drilling locations in a very large, unconventional tight-oil area based on both surface and subsurface characteristics. A North American case study is presented. Operators entering new prospect areas with little well control must make decisions on locating limited numbers of exploratory wells. Large prospect areas can be characterized by a variety of different mineral rights, surface-permitting obligations, uncertainties in geologic subsurface characteristics, complexity of topography, a variety of available infrastructures, potential operational issues, and environmental and regulatory challenges. The decision to appraise an acreage position is very complex and has a high level of risk. One solution to rank and risk candidate drilling locations is a procedure that considers the most critical variables needed for appraisal. The variables are used to generate optimized drilling location scenarios under various uncertainties and constraints using a mathematical probabilistic-optimization procedure and a ‘scorecard’ of surface cultural characteristics.
A national oil company in Ecuador was focused on the exploration and production of hydrocarbon resources within the Ecuadorian territory. It became the main operator in Ecuador after a merger with another operator in 2012. This paper presents the master plan prepared by the operator to reorganize the entire portfolio, ranking assets in terms of value as a first approach to maximize return on investments (ROI). Following the merger, the new organization was attempting to reach equilibrium; however, issues regarding how best to allocate financial and human resources affected production delivery. At that time, risk and uncertainty management became crucial for reorganizing the entire portfolio. A pilot project to address similar issues had been initiated by the operator beginning with Blocks 7 and 21 in 2010, where the value of the different fields in the block had been ranked using the front-end loading (FEL) methodology. This exercise had also been successfully replicated in Blocks 12, 15, and 18. When the merger was finalized, this approach was applied immediately to integrate and rank all assets operated by the newly merged company, allowing upper management to assign resources accordingly. Once the assets were ranked and grouped to identify clearly those fields comprising 60, 30, and 10% value for the operator, the process of calculating the associated investment to capitalize production and additional reserves from those fields began. A matrix to measure ROI vs. risk was then built, and every project was assigned an estimated operational expenditure (OPEX) value associated with the new growth strategy. This tool allowed the operator to select those opportunities that required minimum investment coupled with low and stable operating costs. In several fields, foreign investments were made under a contractual model, allowing service companies to provide specific services to increase oil production and recoverable volume based on a risked investments model, while the operator maintained the decision-making power and operatorship of all assets. As a result of this strategy and in conjunction with a robust management and execution plan, production not only stopped declining but reached higher production levels than previously experienced.
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