# Browsing by Subject "Reservoir management"

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Item Algorithm-aided decision-making in reservoir management(2019-05) Lee, Boum Hee; Lake, Larry W.; DiCarlo, David; Gilbert, Robert; Sepehrnoori, Kamy; Mohanty, KishoreShow more Sound reservoir management involves making decisions in the presence of uncertainty and complexity. Because projects handled in the oil and gas industry are often highly risky and uncertain, the decision-making methods the geoscientists employ must be self-consistent, systematic, and defensible. This dissertation addressed three example problems commonly encountered in reservoir management: water injection allocation optimization, horizontal well refrac scheduling, and infill drilling scheduling. Solutions to each problem employ different algorithms and data analytic techniques that allow a coherent integration of uncertainty and decisions. The specific algorithms and statistical tools used for each problem are provided below. The solution to water injection allocation draws from simple models as well as appropriate statistical methods. The capacitance-resistance model (CRM) is used to model interactions between injectors and producers to help predict the reservoir’s fluid production response. The CRM is paired with Koval’s K-Factor method to decouple oil and water from total fluid production. The models are fitted using a bootstrapped dataset to generate a diverse distribution of history matched solutions. Next, the best injection scheme corresponding to each history matched model is determined using ensemble optimization (EnOpt). Finally, a sampling algorithm called Thompson sampling is called upon to determine the optimal injection scheme while reducing the number of less promising simulations. This way, one can select the best injection scheme that is robust to uncertainties in history matching while simultaneously minimizing the number of simulation runs where it is unnecessary. Validation against a reservoir simulation model is provided at the end to confirm that the injection scheme selected is indeed optimal. The refrac scheduling problem examines a horizontal gas well that is a candidate to refracturing. The analysis employs a real options approach to find the current and future conditions in which refracing is the best decision, as well as to provide an accurate valuation that reflects the managerial flexibility of the project. An algorithm called least-squares Monte Carlo (LSM) will be used to achieve the two goals. In parallel, the Ornstein-Uhlenbeck model is calibrated using the ensemble Kalman filter (EnKF) to account for the gas price changes through time stochastically. The results of the valuations are compared against a myopic Monte Carlo/discounted cash flow (MC-DCF) method to demonstrate that the latter provides an underestimate of the true value. The underestimation results from that the MC-DCF approach neglects the alternatives available in managing the project. The difference between the two estimates of project value is calculated to determine the value of flexibility. Finally, the optimal policies determined is examined to confirm that the recommended response to the realization of uncertainties is intuitively consistent. Finally, a Monte Carlo tree search (MCTS) algorithm is paired with a reservoir simulator to optimize the infill drilling schedule in a reservoir undergoing waterflooding. Because of the permutative nature of sequence-dependent actions, the problem suffers from the curse of dimensionality. MCTS allows the user to find an approximate solution to the scheduling problem that is otherwise intractable. The final optimized schedule specifies 1) whether an infill well should be drilled at candidate locations, 2) whether an injector or producer should be drilled, and 3) when the well should be drilled. A provisional validation is provided at the end by comparing the cumulative oil production and the NPV of the MCTS-optimized schedule against those resulting from randomly generated schedules. Overall, the goal of this dissertation is to demonstrate that different algorithms can be tailored to optimize decisions or policies. The proposed solutions systematically integrate the relevant uncertainties in the analysis as they search for the most preferred action. Such rational approach where uncertainty plays an active role in decision-making provides the geoscientists with the confidence that the final optimized decision is the best action to take. Workflows designed and recommended in this dissertation are strongly preferred over the alternatives where uncertainty and sensitivity analyses are conducted after decisions have already been made using deterministic methodsShow more Item Development of a two-phase flow coupled capacitance resistance model(2014-12) Cao, Fei, active 21st century; Lake, Larry W.Show more The Capacitance Resistance Model (CRM) is a reservoir model based on a data-driven approach. It stems from the continuity equation and takes advantage of the usually abundant rate data to achieve a synergy of analytical model and data-driven approach. Minimal information (rates and bottom-hole pressure) is required to inexpensively characterize the reservoir. Important information, such as inter-well connectivity, reservoir compressibility effects, etc., can be easily and readily evaluated. The model also suggests optimal injection schemes in an effort to maximize ultimate oil recovery, and hence can assist real time reservoir analysis to make more informed management decisions. Nevertheless, an important limitation in the current CRM model is that it only treats the reservoir flow as single-phase flow, which does not favor capturing physics when the saturation change is large, such as for an immature water flood. To overcome this limitation, we develop a two-phase flow coupled CRM model that couples the pressure equation (fluid continuity equation) and the saturation equation (oil mass balance). Through this coupling, the model parameters such as the connectivity, the time constant, temporal oil saturation, etc., are estimated using nonlinear multivariate regression to history match historical production data. Incorporating the physics of two-phase displacement brings several advantages and benefits to the CRM model, such as the estimation of total mobility change, more accurate prediction of oil production, broader model application range, and better adaptability to complicated field scenarios. Also, the estimated saturation within the drainage volume of each producer can provide insights with respect to the field remaining oil saturation distribution. Synthetic field case studies are carried out to demonstrate the different capabilities of the coupled CRM model in homogeneous and heterogeneous reservoirs with different geological features. The physical meanings of model parameters are well explained and validated through case studies. The results validate the coupled CRM model and show improved accuracy in model parameters obtained through the history match. The prediction of oil production is also significantly improved compared to the current CRM model. A more reliable oil rate prediction enables further optimization to adjust injection strategies. The coupled CRM model has been shown to be fast and stable. Moreover, sensitivity analyses are conducted to study and understand the impact of the input information (e.g., relative permeability, viscosity) upon the output model parameters (e.g., connectivity, time constants). This analysis also proves that the model parameters from the two-phase coupled model can combine both reservoir compressibility and mobility effects.Show more Item Direct spatiotemporal interpolation of reservoir flow responses(2006-05) Srinivasan, Shekhar; Srinivasan, SanjayShow more The traditional reservoir modeling workflow consists of first developing a reservoir model, performing flow simulation on that model, verifying the model by performing history matching and finally using the history matched model to make predictions of future performance. In contrast, this research focused on two approaches to directly analyze the spatio-temporal variations of dynamic responses such as pressure and well flowrates and perform interpolation. Both these techniques are anchored to the data at the wells. Therefore, the resultant spatio-temporal predictions of dynamic response are history matched by construction. Interpolation or extrapolation of dynamic response to locations away from wells is possible using both the approaches. Therefore, the proposed approaches can be used to quickly determine optimal location to drill additional wells and to gauge the influence of reservoir management decisions. In the first approach, dynamic responses such as pressure transients are treated as time series data. They are analyzed using wavelets that facilitate multiscale decomposition of pressure signals. Using a wavelet-lifting scheme, the transient signal is decomposed into averages and residuals. The corresponding filter coefficients defining the wavelets are treated as spatial random variables and estimated using geostatistics at locations away from wells. A pressure response is reconstructed at unsampled locations by employing the inverse wavelet transform. In an alternate approach, direct spatiotemporal extrapolation of pressure is performed. The transient pressure data at the wells are first analyzed using correlation measures such as semivariograms. For simplicity, time is taken as another spatial dimension and semivariogram values corresponding to the resultant lag-vectors are inferred and subsequently modeled. Spatiotemporal extrapolation is then performed to obtain the response at any location in space and at any instant in time. The robustness of both these approaches is verified on several case examples.Show more Item A life cycle optimization approach to hydrocarbon recovery(2010-12) Parra Sanchez, Cristina, 1977-; Lake, Larry W.; Bickel, James E.Show more The objective of reservoir management is to maximize a key performance indicator (net present value in this study) at a minimum cost. A typical approach includes engineering analysis, followed by the economic value of the technical study. In general, operators are inclined to spend more effort on the engineering side to the detriment of the economic area, leading to unbalanced and occasionally suboptimal results. Moreover, most of the optimization methods used for production scheduling focus on a given recovery phase, or medium-term strategy, as opposed to an integrated solution that allocates resources from discovery to field abandonment. This thesis addresses the optimization of a reservoir under both technical and economic constraints. In particular, the method presented introduces a life cycle maximization approach to establish the best exploitation strategy throughout the life of the project. Deterministic studies are combined with stochastic modeling and risk analysis to assess decision making under uncertainty. To demonstrate the validity of the model, this document offers two case studies and the optimal times associated with each recovery phase. In contrast with traditional depletion strategies, where the optimization is done myopically by maximizing the net present value at each recovery phase, our results suggest that time is dramatically reduced when the net present value is optimized globally by maximizing the NPV for the life of the project. Furthermore, the sensitivity analysis proves that the original oil in place and non-engineering parameters such as the price of oil are the most influential variables. The case studies clearly show the greater economic efficiency of this life cycle approach, confirming the potential of this optimization technique for practical reservoir management.Show more