Browsing by Subject "Uncertainty analysis"
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Item A probabilistic workflow for uncertainty analysis using a proxy-based approach applied to tight reservoir simulation studies(2016-08) Wantawin, Marut; Sepehrnoori, Kamy, 1951-; Yu, WeiUncertainty associated with reservoir simulation studies should be thoroughly captured during history matching process and adequately explained during production forecasts. Lacking information and limited accuracy of measurements typically cause uncertain reservoir properties in the reservoir simulation models. Unconventional tight reservoirs, for instances, often deal with complex dynamic flow behavior and inexact dimensions of hydraulic fractures that directly affect production estimation. Non-unique history matching solutions on the basis of probabilistic logic are recognized in order to avoid underestimating prediction results. Assisted history matching techniques have been widely proposed in many literature to quantify the uncertainty. However, few applications were done in unconventional reservoirs where some distinct uncertain factors could significantly influence well performance. In this thesis, a probabilistic workflow was developed using proxy-modeling approach to encompass uncertain parameters of unconventional reservoirs and obtain reliable prediction. Proxy-models were constructed by Design of Experiments (DoE) and Response Surface Methodology (RSM). As preliminary screening tools, significant parameters were identified, thus removing those that were insignificant for the reduced dimensions. Furthermore, proxy-models were systematically built to approximate the actual simulation, then sampling algorithms, e.g. Markov Chain Monte Carlo (MCMC) method, successfully estimated probabilistic history matching solutions. An iterative procedure was also introduced to gradually improve the accuracy of proxy-models at the interested region with low history matching errors. The workflow was applied to case studies in Middle Bakken reservoir and Marcellus Shale formation. In addition to estimating misfit function for the errors, proxy-models are also regressed on the simulated quantity of the measurements at various points in time, which is shown to be very useful. This alternative method was utilized in a synthetic tight reservoir model, which analyzed the impact of complex fracture network relative to instantaneous well performance at different stages. The results in this thesis show that the proxy-based approach reasonably provides simplified approximation of actual simulation. Besides, they are very flexible and practical for demonstrating the non-unique history matching solutions and analyzing the probability distributions of complicated reservoir and fracture properties. Ultimately, the developed workflow delivers probabilistic production forecasts with efficient computational requirement.Item Assessing reservoir performance and modeling risk using real options(2012-05) Singh, Harpreet; Srinivasan, Sanjay; Lake, Larry W.Reservoir economic performance is based upon future cash flows which can be generated from a reservoir. Future cash flows are a function of hydrocarbon volumetric flow rates which a reservoir can produce, and the market conditions. Both of these functions of future cash flows are associated with uncertainties. There is uncertainty associated in estimates of future hydrocarbon flow rates due to uncertainty in geological model, limited availability and type of data, and the complexities involved in the reservoir modeling process. The second source of uncertainty associated with future cash flows come from changing oil prices, rate of return etc., which are all functions of market dynamics. Robust integration of these two sources of uncertainty, i.e. future hydrocarbon flow rates and market dynamics, in a model to predict cash flows from a reservoir is an essential part of risk assessment, but a difficult task. Current practices to assess a reservoir’s economic performance by using Deterministic Cash Flow (DCF) methods have been unsuccessful in their predictions because of lack in parametric capability to robustly and completely incorporate these both types of uncertainties. This thesis presents a procedure which accounts for uncertainty in hydrocarbon production forecasts due to incomplete geologic information, and a novel real options methodology to assess the project economics for upstream petroleum industry. The modeling approach entails determining future hydrocarbon production rates due to incomplete geologic information with and without secondary information. The price of hydrocarbons is modeled separately, and the costs to produce them are determined based on market dynamics. A real options methodology is used to assess the effective cash flows from the reservoir, and hence, to determine the project economics. This methodology associates realistic probabilities, which are quantified using the method’s parameters, with benefits and costs. The results from this methodology are compared against the results from DCF methodology to examine if the real options methodology can identify some hidden potential of a reservoir’s performance which DCF might not be able to uncover. This methodology is then applied to various case studies and strategies for planning and decision making.Item Back-calculating emission rates for ammonia and particulate matter from area sources using dispersion modeling(Texas A&M University, 2004-11-15) Price, Jacqueline ElaineEngineering directly impacts current and future regulatory policy decisions. The foundation of air pollution control and air pollution dispersion modeling lies in the math, chemistry, and physics of the environment. Therefore, regulatory decision making must rely upon sound science and engineering as the core of appropriate policy making (objective analysis in lieu of subjective opinion). This research evaluated particulate matter and ammonia concentration data as well as two modeling methods, a backward Lagrangian stochastic model and a Gaussian plume dispersion model. This analysis assessed the uncertainty surrounding each sampling procedure in order to gain a better understanding of the uncertainty in the final emission rate calculation (a basis for federal regulation), and it assessed the differences between emission rates generated using two different dispersion models. First, this research evaluated the uncertainty encompassing the gravimetric sampling of particulate matter and the passive ammonia sampling technique at an animal feeding operation. Future research will be to further determine the wind velocity profile as well as determining the vertical temperature gradient during the modeling time period. This information will help quantify the uncertainty of the meteorological model inputs into the dispersion model, which will aid in understanding the propagated uncertainty in the dispersion modeling outputs. Next, an evaluation of the emission rates generated by both the Industrial Source Complex (Gaussian) model and the WindTrax (backward-Lagrangian stochastic) model revealed that the calculated emission concentrations from each model using the average emission rate generated by the model are extremely close in value. However, the average emission rates calculated by the models vary by a factor of 10. This is extremely troubling. In conclusion, current and future sources are regulated based on emission rate data from previous time periods. Emission factors are published for regulation of various sources, and these emission factors are derived based upon back-calculated model emission rates and site management practices. Thus, this factor of 10 ratio in the emission rates could prove troubling in terms of regulation if the model that the emission rate is back-calculated from is not used as the model to predict a future downwind pollutant concentration.Item Gas storage facility design under uncertainty(2009-12) Ettehadtavakkol, Amin, 1984-; Jablonowski, Christopher J.; Lake, Larry W.In the screening and concept selection stages of gas storage projects, many estimates are required to value competing projects and development concepts. These estimates are important because they influence which projects are selected and which concept proceeds into detailed engineering. In most cases, there is uncertainty in all of the estimates. As a result, operators are faced with the complex problem of determining the optimal design. A systematic uncertainty analysis can help operators solve this problem and make better decisions. Ideally, the uncertainty analysis is comprehensive and includes all uncertain variables, and simultaneously accounts for reservoir behavior, facility options, and economic objectives. This thesis proposes and demonstrates a workflow and an integrated optimization model for uncertainty analysis in gas storage. The optimization model is fast-solving and eliminates most constraints on the scope of the uncertainty analysis. Using this or similar workflows and models should facilitate analysis and communication of results within the project team and with other stakeholders.