Waterflood optimization using streamlines and reservoir management risk analysis with market uncertainty
- Waterflooding is a common oil recovery method in which water is injected into an oil reservoir using strategically placed injectors to maintain pressure and sweep oil to production wells. Waterflood performance of mature fields can be improved significantly by modifying injection and production rates at individual wells. Compared to improving production through infill wells, rate changes are economical and readily implemented. In most traditional optimization methods, the number of evaluations of the objective function at each optimization step is of the same order as the number of control variables. As a result, applying traditional optimization methods to the exploitation of mature waterfloods generally involves elevated computational costs. In the first half of this dissertation, we propose a new optimization method based on flux patterns in which the number of simulations per optimization step is independent of the number of control variables. At each optimization step, our method approximates the complicated objective function of well rates by means of a local linear sensitivity analysis based on the flux patterns generated by streamline simulation or a finite-volume flow diagnostic technique. The generation of the flux patterns requires only a single simulation. This sensitivity analysis allows the oil/water production rates to be estimated as linear functions of well rates, and hence it locally linearizes the objective function. Using the linearized objective function within this optimization step does not require additional simulation until the determination of next optimization step, which reduces the computational cost dramatically compared to traditional optimization approaches. This core idea is also generalized for longterm optimization problems in two ways: one using an analytical decline model and the other using flow fraction information between wells. We demonstrate the method using several waterflooding scenarios. We find solutions that yield good operational strategies at significantly reduced computational cost. The efficiency of the method makes the approach powerful and applicable to mature waterfloods currently operated around the world. While the application of formal optimization techniques in reservoir management has lately received significant attention in the oil industry, the realization of long-term optimum production strategies is still challenging, partially because of the uncertainty associated with the future oil price. In the second half of this dissertation, we propose a risk measure of a given production strategy with respect to the market uncertainty. This measure is interpreted as the value of the knowledge of oil price associated with the assumed stochastic distribution of the uncertain market variables. However, with the computational cost increasing with the number of market scenarios, the computation of this risk measure with reservoir simulation directly is numerically infeasible when the market model is complex. We present a numerical approach to estimate the upper and lower bounds of this risk measure efficiently, where computational cost does not increase with the number of possible oil price scenarios. The tightness of the bounds can be controlled according to the user's computational capability. We also generalize the risk measure and its corresponding estimation approach to the case where the stochastic distribution of market variables is not fully known (i.e. the case with distributional uncertainty). Comparing the risk measured with a base market model to the risk measured with an upgraded market model with additional stochastic information, the difference between these two values of the risk measure implies the monetary value of the additional information in the upgraded market model. This value might be used to decide if it is worthwhile to invest capital that aims at improving the oil price forecast or reducing market uncertainty. Our approach is validated on several fields undergoing waterflooding. In each case we consider a large number of market scenarios to analyze their impact on performing waterflooding optimization, and we estimate the monetary value associated with different degrees of uncertainty in market forecasts.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|Stanford University, Institute for Computational and Mathematical Engineering.
|Thiele, Marco Roberto, 1963-
|Thiele, Marco Roberto, 1963-
|Statement of responsibility
|Submitted to the Institute for Computational and Mathematical Engineering.
|Thesis (Ph.D.)--Stanford University, 2014.
- © 2014 by Tailai Wen
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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