..., respectively, are obtained from IMPES-like pressure equations and a Riemann solver that include gravity effects. With history-matched model(s) as the forward model(s), we estimate the optimal well controls (pressure or rates at control steps) that maximize the life-cycle net-present-value (NPV) of production...

... Abstract A new methodology for the joint optimization of economic project life and time-varying well controls is introduced. The procedure enables the maximization of net present value (NPV) subject to satisfaction of a specified modified internal rate of return. Use of this framework allows...

... and well control variables u . The vector z ∈ ℤ denotes discrete categorical variables, which could designate, for example, whether a well is an injector or a producer. Here n 1 and n 2 denote the number of optimization variables for well placement and well control, respectively. For problems...

... correlation on the controls at each well. In this approach, well controls are parameterized in terms of a few optimization parameters which significantly reduces the dimension of the joint optimization problem. Moreover, the imposed smoothness on the well controls will result in temporally smooth well...

... is to develop implementations that can be applied for water flooding optimization under geological uncertainty where we wish to develop well controls that satisfy two objectives: the first is to maximize the expectation of life-cycle net-present-value (NPV) (commonly referred to as robust optimization...

... to estimate the optimal well controls which maximize life-cycle net-present-value (NPV). By optimizing the well controls, we implicitly optimize the WAG ratio (volume of water injected divided by the volume of gas injected). We apply the optimization methodology to synthetic, channelized-reservoir examples...

..., where seismic and production data were history-matched using ensemble-based approaches. The well controls are optimized for the next ten years of production using the multiscale techniques Hi-MO and RHi-MO. Our approaches are successfully compared against a reference case, which applies the well rates...

... functions of optimization variables (well controls) as they involve the outputs or states of the reservoir system. Following the work of Liu and Reynolds (2014c) , we jointly maximize the expectation and minimize the associated risk under field constrains. The gradient based weighted sum method ( Gass...

... the gradient of the net-present-value (NPV) function and the cross-covariance of well controls and the net-present-value functional provides the basis for EnOpt ( Chen et al., 2009 ). Recently, Do (2012) showed that EnOpt, a preconditioned simplex gradient and a modified SPSA algorithm are theoretically...

.... optimization problem objective function PSO Artificial Intelligence field development well control Upstream Oil & Gas Simulation optimization iteration optimization variable relaxation constraint particle spe 163631 procedure production well configuration Optimization Method well...

..., without requiring correlation between well controls to provide smoothness. During the optimization procedure, the control steps are defined on a well-by-well basis considering the difference between well controls in two consecutive control steps and the gradient of the objective function with respect...

..., as some optimization algorithms require prohibitive work to compute the gradients of the objective function with respect to the well controls. These methods also require access to the simulator code, which makes them difficult to use with commercial software. Another challenge is to find a reasonable...

... an estimate of optimal controls that gives the highest NPV in two of the three examples and within 0.1% of the highest value in the third example. reservoir simulation approximation well control optimization problem objective function enopt artificial intelligence pattern search method control...

... (lifecycle) value with respect to all (time-dependent) well control variables in one go, at a cost less than that of an extra reservoir simulation run. These sensitivities can be used in an optimization loop to iteratively improve well controls. We implemented the adjoint method and an associated...