Maximizing Asset Value by Waterflooding Based on a Hypothetico-Deductive Method for Formulation, Evaluation, and Selection of Exploitation Scenarios. Case Study Lower Lagunillas-03 Reservoir

SPE Europec Featured at 78th EAGE Conference and Exhibition

et al. 2016

The industry is undergoing a transformational change, driven by increases in exploration and development costs in the last decade, combined with falling oil prices in recent months. As a result, there is an increased interest in squeezing more from mature assets. The general message is that a 1% increase in recovery factor could result in two additional years of global oil and gas supply; however, industry experts know how difficult it is to increase recovery from mature fields. This paper presents a novel screening method that enables a large number of scenarios to be considered and facilitates convergence on potential solutions. By 2018, 70% of global oil production (more than 80 million B/D) is projected to come from mature fields (Fig. 1). Some major exploration and production (E&P) operators already have more than 70% of their portfolios in production decline. This global trend, combined with the pressure to reduce the cost per BOE resulting from declining oil prices, has pushed E&P operators to place a renewed emphasis on mature fields. This paper presents an integrated approach to revitalizing mature fields and addresses this new reality by focusing on three key areas: immediate impact solutions, reservoir optimization, and new pay. The first step in this process is a screening and ranking method for mature field revitalization choices, which enables feasible options to be identified at an early stage, thus avoiding wasted time and money pursuing unfeasible and/or uneconomic options. A common challenge for the operators of mature assets is filtering the vast array of issues and potential solutions. This unique screening method leverages a global reservoir analog screening database combined with technical and economic modeling techniques to help ensure that the optimum revitalization strategy for the reservoir is achieved. In many cases, the proposed solutions may be technically interesting, but very expensive or operationally not feasible. This paper explains how the proposed revitalization plans must be combined with a fully integrated end-to-end solution, from planning, design, and simulation to field implementation. Case studies are shared to demonstrate how this integrated approach, linking concept to implementation, can avoid expensive iterations and help to enable mature field production uplift.

Maximizing Mature Field Production - A Novel Approach to Screening Mature Fields Revitalization Options

O’Brien

Sayavedra

SPE Europec Featured at 78th EAGE Conference and Exhibition

et al. 2016

New Trends in Waterflooding Project Optimization

SPE Latin America and Caribbean Petroleum Engineering Conference

Lokhandwala

Tillero

2017

Water is the most commonly used injection fluid for flooding/energizing oil reservoirs. Despite oil price fluctuations, water use has continued because of its wide availability, relatively low cost, and ease of handling. Decades of research and field application experiences have yielded a sound theoretical approach and practical knowledge of the subject. Nevertheless, water injection deployment and operations can still benefit from optimization. This paper discusses the state-of-the-art use of numerical optimizers based on smart algorithms and stochastic machines that couple subsurface, surface, and economic models. During planning and operations of waterflooding projects, many decisions are made, such as the number, location, and drilling sequence of new injector and producer wells, total and per well injection rates, well conversion, and fluid withdrawal rates. In addition, each decision variable has multiple options, which combined can generate hundreds or thousands of scenarios, raising the key question of how the optimum scenario can be determined in a timely manner. Furthermore, the optimum scenario selection process should consider uncertainty (e.g., reservoir properties and oil prices) as well as operational constrains. Based on previous experience, a general workflow was developed and fine-tuned to help identify optimum scenarios. The workflow begins by defining the scenario matrix using available validated history-match models. Models are coupled with an automatic optimizer/stochastic machine. The study cases considered reservoirs with heavy-to-medium oil, injection by pattern and flank, large variations in original oil in place (OOIP), and number of wells for waterflooding implementation and reactivation planning. Optimization runs typically require hundreds of iterations to approach the maximum or minimum objective business function. Each iteration corresponds to a scenario. To identify the optimal scenario quickly, various strategies were tested: parallel computing and new methodologies of sequential optimization with reduced number of decision variables, initial exploratory runs with a shortened economic horizon time, and stochastic analysis of selected scenarios of the optimization run. All of these strategies proved successful, depending on the specific situation. The workflow application in three case studies yielded approximately 30% cumulative production and net present value (NPV) increments, with less economic risk than the traditional deterministic simulation approach and reduced water cut up to 40%; compared to base scenarios, Np and NPV increases higher than 200% were obtained. Furthermore, the workflow application generated a large number of scenarios that provided flexibility to modify operations during unexpected events. Optimizers/stochastic machines were determined to be a valid means to quantitatively estimate the economy and risks and are a fundamental tool for managing waterflooding projects, resulting in better scenarios than the traditional deterministic approach. The approach is also applicable to all types of enhanced oil recovery (EOR) projects.

Comparative Analysis of Data-Driven, Physics-Based and Hybrid Reservoir Modeling Approaches in Waterflooding

Day 2 Tue, October 04, 2022

Tillero

Calad³

et al. 2022

Hydrocarbon production optimization is essential in pursuing the best scenarios for economic outcomes. But because of complex and multi-dimensional nature of production processes, thousands of scenarios are possible. Extensive data collection may allow uncovering patterns still unidentified. With on-site computing power increasing, cloud availability, and artificial intelligence evolution, mathematical optimization methods are becoming powerful and accessible. Data type-tailored models are implemented for history matching and prediction of operational efficiency of the asset. This paper presents a comprehensive analysis and comparison of three data type-tailored reservoir modeling methods and their optimization process for waterflooding field cases. The mathematical techniques used were Data-Driven Capacitance Resistance Model (CRM), Numerical Simulators (Data-Physics) coupled to Smart Algorithms Optimizers, and Hybrid Model (Machine Learning Physics-Based). They were compared to 1-identify the benefits of mathematical optimization techniques, 2-illustrate the methods developed to sort out time and computing capacity restrictions, and 3-validate the techniques by comparing the forecast with actual results. The six study cases of different reservoir types in Argentina, Venezuela, and the USA, had different types data availability. Four had no static model. In two cases, field results were available to confirm the accuracy of the forecasted injection and production. The forecasted increase in Net Present Value (NPV) and cumulative oil production (Np) ranged to 30%, and optimized water injection rates decreased by 50%. Traditional modeling techniques yielding unreliable result in one field with hundreds of producing layers and unknown lateral and vertical continuity were solved using a machine learning technique. In some cases, they pointed toward non-intuitive infill drilling sequence and injection water redistribution. Also, they pointed to options that reduce economic risk. The methods yielded many better economic scenarios and increased the flexibility of operationalizing plans. In one field requiring excessive computing power, using time horizons reduction and successive year-by-year optimization yielded 4 times the NPV of the base case. This approach solves objections related to long computing time and system instability. With the three mathematical techniques, the asset value could be continually maximized by a novel implementation of a heuristic decision-making approach that continuously challenge the current scenario. It makes a systematic formulation of conceivable new scenarios, competing through an objective function determining the probity of compared scenarios. The optimization also resulted in an up to 50% decrease in water injection requirements and the same percentual CO2 emissions reduction.