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Summary Normally only approximately 30% of the oil in a reservoir is extracted during primary production, but using secondary-production methods such as water or gas injection, it is often possible to increase that percentage significantly and maintain the production rate of a reservoir over a longer period of time. In reservoirs under water or gas injection, additional gains can be obtained through an efficient strategy for management of front movement and reservoir sweep. The objective of reservoir production optimization is to maximize an outcome such as sweep efficiency or net present value (NPV) through the control of completion rates or pressures. Using optimization methods, it is possible to compute control settings that result in increased oil production and decreased water production compared with production from standard practices. In this paper, we focus on optimization using sequential quadratic programming (SQP) with an ensemble-based approach to estimate the gradient for the optimization. Although uncertainty in reservoir properties is usually important for the computation of optimal controls, here we use a single realization of the reservoir to evaluate the efficiency of the optimization algorithm. The most expensive aspect of gradient-based optimization is usually the computation of gradients. Most practical production-optimization problems involve large-scale, highly complex reservoir models with thousands of constraints, which makes numerical calculation of the gradient time consuming. Here, we use an ensemble-based approach for finding gradients and use localization to improve estimation of the gradient from a small number of realizations. The Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is used for maximizing the objective function, with the Hessian estimated from a sequence of estimates of the gradient. Improving the gradient approximation using localization results in improvement to the Hessian approximation. A second important aspect of the efficiency of the method is the identification of active constraints. In this paper, we use a method for eliminating nonnegativity constraints to decrease computation time and an updating procedure to solve each iteration of SQP much faster than the base case. Both the speed of the algorithm and the final NPV were increased significantly. We evaluate the method by applying it to optimization of control settings in the Brugge field. Brugge is a 3D synthetic model designed by TNO with 20 vertical producers and 10 vertical peripheral water injectors. All of the producers and injectors are smart wells whose downhole chokes must be adjusted to optimize NPV. The total number of completion flow rates to be controlled is 84 at each timestep, with 40 timesteps (every 6 months). There are 1,200 inequality constraints on total well liquid rates and 3,360 nonnegativity constraints on completion liquid rates. There are also inequality constraints on the bottomhole pressure (BHP) for wells at each time period.
- North America > United States > Oklahoma (0.28)
- North America > United States > Texas (0.28)
- North America > United States > California (0.28)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Waterflooding (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
Spontaneous Potentials in Hydrocarbon Reservoirs During Waterflooding: Application to Water-Front Monitoring
Jackson, M.D.. D. (Imperial College London) | Gulamali, M.Y.. Y. (Imperial College London) | Leinov, E.. (Imperial College London) | Saunders, J.H.. H. (Imperial College London) | Vinogradov, J.. (Imperial College London)
Summary Spontaneous potential (SP) is routinely measured using wireline tools during reservoir characterization. However, SP signals are also generated during hydrocarbon production, in response to gradients in the water-phase pressure (relative to hydrostatic), chemical composition, and temperature. We use numerical modeling to investigate the likely magnitude of the SP in an oil reservoir during production, and suggest that measurements of SP, using electrodes permanently installed downhole, could be used to detect and monitor water encroaching on a well while it is several tens to hundreds of meters away. We simulate the SP generated during production from a single vertical well, with pressure support provided by water injection. We vary the production rate, and the temperature and salinity of the injected water, to vary the contribution of the different components of the SP signal. We also vary the values of the so-called "coupling coefficients," which relate gradients in fluid potential, salinity, and temperature to gradients in electrical potential. The values of these coupling coefficients at reservoir conditions are poorly constrained. We find that the magnitude of the SP can be large (up to hundreds of mV) and peaks at the location of the moving water front, where there are steep gradients in water saturation and salinity. The signal decays with distance from the front, typically over several tens to hundreds of meters; consequently, the encroaching water can be detected and monitored before it arrives at the production well. Before water breakthrough, the SP at the well is dominated by the electrokinetic and electrochemical components arising from gradients in fluid potential and salinity; thermoelectric potentials only become significant after water breakthrough, because the temperature change associated with the injected water lags behind the water front. The shape of the SP signal measured along the well reflects the geometry of the encroaching waterfront. Our results suggest that SP monitoring during production, using permanently installed downhole electrodes, is a promising method to image moving water fronts. Larger signals will be obtained in low-permeability reservoirs produced at high rate, saturated with formation brine of low salinity, or with brine of a very different salinity from that injected.
- Europe (1.00)
- North America > United States > Texas (0.46)
- Geology > Geological Subdiscipline > Economic Geology > Petroleum Geology (0.70)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.70)