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.
Successfully implementing polymer flooding and maximizing benefits requires selecting best options of variables such as polymer concentration and slug, and number and location of new wells. Option-decisions combined generate thousands of scenarios. Therefore, even using smart algorithm optimizers to efficiently find the maximum of a business objective mathematical function can be a very time-consuming process. The objective of this article is to demonstrate a methodology to improve the chances of finding the maximum net present value (NPV) solution for planning an offshore polymer-flooding; this includes finding, for example, the minimum injection volume to more easily offshore operations. The reservoir and economy models included here were automatically coupled with software that encloses a smart numerical algorithm for searching complex maximum/minimum functions. The options of the previously mentioned decision variables were selected to maximize the NPV of the inverted five-spot polymer-flooding project under constrained rig availability. The process was conducted in three stages: Stage 1: potential value estimation Stage 2: narrowing options through deterministic numerical simulation Stage 3: A) numerical optimization of all decision variable options except the drilling sequence; B) numerical optimization of the drilling sequence A total of 379 scenarios were numerically forecasted in just a few months. The best scenario showed three times the NPV of the nonflooding case. Compared to the reference water-flooding scenario (i.e., all the same options but with the fluid injected), the NPV was 1.3 times greater, the water-oil ratio (WOR) was 0.45 times lower, and Np was 1.25 times greater. Unobvious scenarios, such as reducing the yearly drilling rig availability but extending drilling by four years, were revealed. A comparison of the working time for Stage 2 with Stages 3A and 3B showed that the numerical optimization is six times faster per scenario generated. This study demonstrates that the use of numerical algorithms of polymer flooding yields a significant incremental value over traditional deterministic simulations in a much shorter time frame and with fewer costs compared to previous steps related to building a reservoir model. It is expected to be applicable to all types of enhanced oil recovery (EOR) processes.
The Lower Lagunillas-03 reservoir, in Maracaibo Lake, Venezuela, has been producing for more than 80 years from La Rosa (25 API gravity) and Lower Lagunillas (19 API gravity) formations. The average pressure is less than a 1/3 of its oil bubble point pressure; whereas more than 70% of the OOIP remains in place. Several waterflooding projects (Modules) with different injection patterns have been implemented since last 40 years, which have shown different results in term of production behavior and oil recovery. Therefore, an optimal waterflooding scheme has not been identified yet for the fifth waterflooding module (Lower Lagunillas Formation) and also for future waterflooding modules to be implemented for revitalizing the potential of this mature reservoir. It is essential in this case to ascertain the objective of an optimal waterflooding scheme in order to unlock the promissory oil recovery potential considering current reservoir conditions and financial resources. This was accomplished based on an innovative hypotetico-deductive method, which considers cycles of formulation-testing-analysis-emerging of hypotheses (scenarios), and starts with the formulation of a simple or relevant hypothesis (expectation) about a feasible exploitation plan. It is tested using a numerical or analytical model campaigned with economical optimization workflow and its results inquires to evaluate the hypothesis in the light of initial expectations as discarded or chosen, or whether some emerging hypotheses might be conducted and others might not. In turn, they are cycled until analysis of prediction determines probity of hypotheses or the refined research of hypotheses is stopped. Contrary to initial expectations, many hypotheses about waterflooding patterns for the fifth module were tested, such as new horizontal injector-producer wells (direct line drive), inverted seven-spot, and inverted five-spot from existing wells. Nevertheless a substantially increased Net Present Value (2 times greater compared to Base Case) was reached by testing inverted five-spot patterns using infill drilling and extending the area to be flooded so that it emerges as a novel strategy for unlock the potential of this mature reservoir.
Generating an integrated conceptual probabilistic model forecast for exploration of hydrocarbon fields is essential to the decision-making process when developing a strong portfolio of exploration prospects. It is important not only when considering the potential volume that could be discovered within each prospect, but also when considering drilling plans for extracting these reserves. It is important to both define the best strategies for achieving strong growth and sustainable profit over time and to consider possible risks and uncertainties that could impact such results. Southeast of Lake Maracaibo, the most important axis of production growth is located within the western part of Venezuela, formed by the Ceiba, Tomoporo and Franquera fields, where a set of exploratory prospects comprise 36 potential reservoirs. Exploratory wells are planned to validate the estimated reserves of the development to help increase oil production. The optimization and prioritization of exploration prospects provides the foundation for creating an interactive workflow that is automated, versatile, and innovative to help optimize the portfolio of the defined prospects. This workflow provides a strategy based on the generation and use of a probabilistic conceptual reservoir model based on information from neighboring fields and exploratory studies. Using this approach, the initial potential for each well and each of the prospects' different production profiles can be probabilistically calculated based on the development strategy. This allows visualization of how many wells should be drilled, the capabilities of the surface facilities, the number of personnel required to operate the field, and other additional important aspects. This conceptual probabilistic model forecast (prospects—wells—surface) is connected to an economic-risk-uncertainty model, creating fully integrated modeling. When new information is gained, automated adjustments can be made, thus achieving quick optimal viewing of opportunities within the portfolio of prospects and improving decision making.
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