Advanced Reservoir Management Research Articles

Management of Tropical River Basins and Reservoirs under Water Stress: Experiences from Northeast Brazil

Due to global warming, a reduction in available water will occur in many watersheds and conflicts concerning water use will take place. This situation is already typical in semi-arid areas, where many reservoirs have been constructed for water storage. Increased energy demands and climate change have led to severe and increasing pressure on aquatic systems. Today, the environmental policies of many countries, such as Brazil, give priority to constructing new reservoirs for hydropower use, and an adopted reservoir and river basin management plan must minimize environmental impacts. Moreover, the production of energy plants will promote more requirements for new dam projects. The Itaparica reservoir is 30 years old, located in the São Francisco river in Northeast Brazil, and is the focus of an environmental study. The article focus of investigations is on the environmental and social impacts after the construction of Itaparica reservoir, governance difficulties, and adopted actions to minimize those impacts. Significant environmental impacts are recognizable, such as increased sedimentation in the inflow area, damage to the lakeshore zone by operational water level variation, water losses by evaporation and infiltration, and degradation of inundated vegetation. Furthermore, a trophic upsurge has been registered with severe eutrophication processes, such as the occurrence of cyanobacteria, oxygen deficit in the hypolimnion, and mass development of macrophytes (Egeria densa). With the creation of the dam there was compulsory displacement of the population of the municipalities around Itaparica reservoir with consequent difficulties of adaptation in the new spaces. Furthermore, there was de-structuring of social relation networks, loss of arable land and improvements, and small and insufficient indemnities for land. In this context, concepts for an advanced reservoir management under consideration of water scarcity are presented and discussed. This study aims to contribute to sustainable reservoir management

Advanced reservoir modeling and management of complex reservoirs

Advanced reservoir modeling and management of complex reservoirs

Advanced Reservoir Management to Maximize Hydrocarbon Recovery in Mature and Geologically Complex Reservoirs

This article presented key elements of a successful reservoir management process in a complex reservoir. The authors proposed an advanced assisted automatic history matching approach by combining global and local optimization to construct a reliable model for complex reservoirs. This method not only significantly improves convergent speed but also overcomes the main limitation of a gradient-based method in the inverse problems. The results from experiments in laboratory scale and full field simulation proved that polymer flooding is a promising approach to enhanced oil recovery in this reservoir.

Advanced Reservoir-Management Workflow - EnKF-Based Assisted-History-Matching Method

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 118906, ’Advanced Reservoir-Management Workflow Using an EnKF-Based Assisted-History-Matching Method,’ by A. Seiler, G. Evensen, J.-A. Skjervheim, J. Hove, and J.G. Vabo, StatoilHydro, prepared for the 2009 SPE Reservoir Simulation Symposium, The Woodlands, Texas, 2-4 February. The ensemble Kalman filter (EnKF) is a tool to assist history matching. It uses sequential processing of measurements, is capable of handling large parameter sets, and it solves the combined state- and parameter-estimation problem. The proposed workflow was applied to a complex North Sea oil field. The EnKF provided an ensemble of history-matched reservoir models. The updated ensemble was used to predict the uncertainty in future production. Introduction Reservoir modeling and history matching aim to deliver integrated reservoir models for reservoir-management purposes. These reservoir models must reproduce the historical field performance and must be consistent with all available static data (e.g., core, well-log, and seismic) and dynamic data (e.g., well-production, tracer-concentration, and 4D-seismic). Also, they should integrate current information about the reservoir and the associated uncertainty to enable real-time decisions. The EnKF was introduced for updating nonlinear ocean models and is a Monte Carlo approach in which errors are represented by an ensemble of realizations. Model parameters and state variables are updated sequentially as new measurements become available. The result is an updated ensemble of realizations, conditioned to all production data, that provides an improved estimate of the model parameters and state variables and their uncertainty. Previously, the focus was on estimating porosity and permeability fields in the simulation models. Here, parameters such as initial fluid contacts and fault- and vertical-transmissibility multipliers are included as additional uncertain parameters to be estimated. The full-length paper details an EnKF method for history matching reservoir-simulation models and discusses its relation to traditional methods. Assisted History Matching The EnKF method is an alternative to traditional assisted history matching. Conditioning reservoir stochastic realizations to production data is described generally as finding the optimal set of model parameters that minimizes the misfit between a set of measurements and the corresponding responses calculated on the realization of the stochastic model. However, it also can be formulated in a Bayesian framework as finding the posterior probability-density (PD) function of the parameters and the model state, given a set of measurements and a dynamic model with known uncertainties. The Bayesian formulation is the common starting point for traditional minimization methods and sequential-data-assimilation methods (detailed in the full-length paper). EnKF History-Matching Workflow The history-matching workflow, independent of the method used, involves three major steps. First, parameterization of the uncertain parameters, which are a characterization of the major uncertainty of the model solution, is identified. Then, a prior-error model is specified for the selected parameters on the basis of an initial uncertainty analysis. Finally, a solution method must be selected. All three steps may be equally important, and the selections and choices made will depend on the problem at hand.

Applications of permanent sensors for real-time well management

Advanced well and reservoir management techniques have been developed to optimize the recovery of modern, complex completions. Permanent sensors deployed in the wellbore and along the tubing, in the surface production network, and in the facilities provide a rich data flow to support these techniques. Sensor evolution from mechanical devices through electronic systems to optical technologies has increased the data flow supporting production operations to unprecedented levels. Sensors in today's wells measure pressure, temperature, oil, gas and water flow rates, resistivity, and seismic signals.

Integrated Reservoir Management for the Long Term-The Carpinteria Offshore Field

The Carpinteria offshore field, Santa Barbara, California, has produced more than 100 million barrels of oil to date. This mature field has continued operations in an economically and politically challenging environment that finally resulted in the abandonment of the field's California state leases by the lease holder. The aban doned leases, together with adjoining federal leases, are now operated by an independent producer. Los Alamos National Laboratory has joined with that independent operator, Pacific Operators Offshore, and with the State Lands Com mission of California and the Minerals Management Service in a unique collabora tive effort to redevelop the mature field. This project is a part of a larger umbrella project, the Advanced Reservoir Management project (ARM), that is designed to demonstrate the worth of advanced computational tools and state-of-the-art methods for independent oil and gas producers. The Carpinteria Reservoir Redevelop ment project takes a long-term view of reservoir management; as a result, our management plan includes a continuing investment in time and technology to understand the reservoir better. In particular, we have completed an extensive reservoir characterization and geological modeling effort that has created a self-con sistent model, satisfying geophysical, geological, and engineering data constraints. We have begun the engineering-intensive flow simulation phase of the project using the current geological description of the reservoir and are confident that our careful efforts in geological modeling will result in a reasonable reservoir flow model. Dynamic documents exist that are used by participants to stay abreast of developments on the project. These World Wide Web (www) pages may be viewed at http://www.ees.lanl.gov/EES5/arm/pooi. Other sites of interest that describe the nature of the agencies and companies involved in the project are at http://www. /ees.lanl.gov/EES5/, http://www.mms.gov/omm/pacific/index.html, http: //www.slc.ca.gov, and http://www.pacops.com.

Economically improving oil recovery by advanced reservoir management

In oil fields exploited by water or immiscible-gas injection the application of the advanced reservoir management (ARM) technique allows high volumetric flooding efficiencies (and therefore high oil recovery factors) with a moderate increase in cost and without making recourse to enhanced oil recovery (EOR) processes. Following a short analysis of the characteristics and drawbacks of the various EOR processes, the ARM technique is described in detail. This technique calls for the use, all through field life, of the most advanced well and interwell testing and logging methods. The geological, or “static” model of the reservoir (which is built from outcrop studies and well data gathered during the field development phase) is modified and validated based on interwell connectivity data and displacing fluid front position recorded throughout the field life. The “dynamic” numerical model of the reservoir obtained in this way is continuously updated and is used for engineering remedial action (including the drilling of infill wells) aimed at maximizing reservoir rock coverage by the injected fluid(s). A mandatory prerequisite for oil field exploitation by the ARM technique is the availability of a team of experts covering all disciplines related to reservoir geology and engineering. The experts work synergetically together from the very moment the field is discovered up to its abandonment.

Arm (Advanced Reservoir Management) Vs. Eor

Advanced Reservoir Management (ARM) techniques aimed at a better reservoir coverage by injected fluid(s) through the improvement of interwell connectivity and recourse to gravity drainage are shown to have a better chance than EOR techniques in improving oil recovery with satisfactory economic results.