# Browsing by Subject "Value of information"

Now showing 1 - 8 of 8

###### Results Per Page

###### Sort Options

Item Assessing reservoir performance and modeling risk using real options(2012-05) Singh, Harpreet; Srinivasan, Sanjay; Lake, Larry W.Show more 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.Show more Item Facility planning and value of information : a case study of deepwater reservoir compartmentalization(2010-08) Ramachandran, Hariharan, 1986-; Jablonowski, Christopher J.; Lasdon, Leon S.Show more This thesis investigates how estimates of reservoir compartmentalization impact facility sizing decisions and the degree to which inaccurate estimates destroy project value. An uncertainty analysis workflow is proposed and an asset development optimization model is specified to simulate the decision making process during concept selection. The model endogenizes drilling decisions and includes a real option to expand facility capacity after the uncertain variables are realized. The value of information analysis suggests that cost of erroneous estimates of reservoir compartmentalization is significant and can reduce asset value by more than 30%. We also find that the negative impacts are larger when the degree of compartmentalization is underestimated (too optimistic) than when it is overestimated (too pessimistic).Show more Item Facility planning and value of information using a tank reservoir model : a case study in reserve uncertainty(2010-05) Singh, Ashutosh; Jablonowski, Christopher J.; Groat, Charles G.Show more This thesis presents a methodology to incorporate reservoir uncertainties and estimate the loss in project value when facility planning decisions are based on erroneous estimates of input variables. We propose a tank model along with integrated asset development model to simulate the concept selection process. The model endogenizes drilling decisions and includes an option to expand. Key decision variables included in the model are number of pre-drill wells, initial facility capacity and number of well slots. Comparison is made between project value derived under erroneous estimates for reserve size and under an alternate hypothesis. The results suggest loss in project value of up to 40% when reservoir estimates are erroneous. Moreover, both optimistic and pessimistic reserve estimates results in a loss in project value. However, loss in project value is bigger when reserve size is underestimated than when it is overestimated.Show more Item The mathematics of hedging(2009-12) Chen, Yi-Jen Elaine; Jablonowski, Christopher J.; Groat, Charles G.Show more Possessing the knowledge to hedge energy price risks properly is essential and crucial for running a long-term business. In the past, many hedging instruments have been invented and widely used. By using these derivatives, decision makers reduce the price risk to a certain degree. To apply these hedging instruments to the perfect hedging strategies correctly, it is necessary to be familiar with these tools in the first place. This work introduces the financial tools widely applied in hedging, including forward contracts, futures, swaps and options. It also introduces the hedging strategies used on energy hedging. Since individuals are creating strategies according to their unique risk appetite and collected information, this work presents three risk appetites and a method of distinguishing valuable information. With the contribution of this thesis, future works can be done in the field that connect the information valuation and energy hedging by changing the behavior in each risk appetites’ hedging ratio.Show more Item Optimization of production allocation under price uncertainty : relating price model assumptions to decisions(2011-08) Bukhari, Abdulwahab Abdullatif; Jablonowski, Christopher J.; Lasdon, Leon S.; Dyer, James S.Show more Allocating production volumes across a portfolio of producing assets is a complex optimization problem. Each producing asset possesses different technical attributes (e.g. crude type), facility constraints, and costs. In addition, there are corporate objectives and constraints (e.g. contract delivery requirements). While complex, such a problem can be specified and solved using conventional deterministic optimization methods. However, there is often uncertainty in many of the inputs, and in these cases the appropriate approach is neither obvious nor straightforward. One of the major uncertainties in the oil and gas industry is the commodity price assumption(s). This paper investigates this problem in three major sections: (1) We specify an integrated stochastic optimization model that solves for the optimal production allocation for a portfolio of producing assets when there is uncertainty in commodity prices, (2) We then compare the solutions that result when different price models are used, and (3) We perform a value of information analysis to estimate the value of more accurate price models. The results show that the optimum production allocation is a function of the price model assumptions. However, the differences between models are minor, and thus the value of choosing the “correct” price model, or similarly of estimating a more accurate model, is small. This work falls in the emerging research area of decision-oriented assessments of information value.Show more Item A value of information analysis of permeability data in a carbon, capture and storage project(2012-05) Puerta Ortega, Carlos Andres; Bickel, J. Eric; Hovorka, Susan; Rai, VarunShow more Carbon dioxide capture and storage (CCS) is considered one of the key technologies for reducing atmospheric emissions of CO₂ from human activities (IPCC, 2005). The scale of potential deployment of CCS is enormous spanning manufacturing, power generation and hydrocarbon extraction worldwide. Uncertainty, cost-benefit challenges, market barriers and failures, and promotion and regulation of infrastructure are the main obstacles for deploying CCS technology in a broad scale. In a CCS project, it is the operator’s responsibility to guarantee the CO₂ containment while complying with environmental regulations and CO₂ contractual requirements with the source emitter. Acquiring new information (e.g. seismic, logs, production data, etc.) about a particular field can reduce the uncertainty about the reservoir properties and can (but not necessarily) influence the decisions affecting the deployment of a CCS project. The main objective of this study is to provide a decision-analysis framework to quantify the Value of Information (VOI) in a CCS project that faces uncertainties about permeability values in the reservoir. This uncertainty translates into risks of CO₂ migration out of the containment zone (or lease zone), non-compliance with contractual requirements on CO₂ storage capacity, and leakage of CO₂ to sources of Underground Source of Drinking Water (USDW). The field under analysis has been idealized based on a real project located in Texas. Subsurface modeling of the upper Frio Formation (injection zone) was conducted using well logs, field-specific GIS data, and other relevant published literature. The idealized model was run for different scenarios with different permeability distributions. The VOI was quantified by defining prior scenarios based on the current knowledge of a reservoir, contractual requirements, and regulatory constraints. The project operator has the option to obtain more reliable estimates of permeability, which will help to reduce the uncertainty of the CO₂ behavior and storage capacity of the formation. The accuracy of the information gathering activities is then applied to the prior probabilities (Bayesian inference) to infer the value of such data.Show more Item Value of information and portfolio decision analysis(2013-08) Zan, Kun; Bickel, J. EricShow more Value of information (VOI) is the amount a decision maker is willing to pay for information to better understand the uncertainty surrounding a decision, prior to making the decision. VOI is a key part of decision analysis (DA). Especially in this age of information explosion, evaluating information value is critical. VOI research tries to derive generic conclusions regarding VOI properties. However, in most cases, VOI properties rely on the specific decision context, which means that VOI properties may not be generalizable. Thus, instead, VOI properties have been derived for typical or representative decisions. In addition, VOI analysis as a method of DA has been successfully applied to practical decision problems in a variety of industries. This approach has also been adopted as the basis of a heuristic algorithm in the latest research in simulation and optimization. Portfolio Decision Analysis (PDA), rooted in DA, is a body of theories, methods, and practices that seek to help decision makers with limited budget select a subset of candidate items through mathematical modeling that accounts for relevant constraints, preferences, and uncertainties. As one of the main tools for resource allocation problems, its successful implementation, especially in capital-intensive industries such as pharmaceuticals and oil & gas, has been documented (Salo, Keisler and Morton 2011). Although VOI and PDA have been extensively researched separately, their combination has received attention only recently. Resource allocation problems are ubiquitous. Although significant attention has been directed at it, less energy has been focused on understanding the VOI within this setting, and the role of VOI analysis to solve resource allocation problems. This belief motivates the present work. We investigate VOI properties in portfolio contexts that can be modeled as a knapsack problem. By further looking at the properties, we illustrate how VOI analysis can derive portfolio management insights to facilitate PDA process. We also develop a method to evaluate the VOI of information portfolios and how the VOI will be affected by the correlations between information sources. Last, we investigate the performance of a widely implemented portfolio selection approach, the benefit-cost ratio (BCR) approach, in PDA practice.Show more Item Value of information and the accuracy of discrete approximations(2010-08) Ramakrishnan, Arjun; Bickel, J. Eric; Lake, Larry W.Show more Value of information is one of the key features of decision analysis. This work deals with providing a consistent and functional methodology to determine VOI on proposed well tests in the presence of uncertainties. This method strives to show that VOI analysis with the help of discretized versions of continuous probability distributions with conventional decision trees can be very accurate if the optimal method of discrete approximation is chosen rather than opting for methods such as Monte Carlo simulation to determine the VOI. This need not necessarily mean loss of accuracy at the cost of simplifying probability calculations. Both the prior and posterior probability distributions are assumed to be continuous and are discretized to find the VOI. This results in two steps of discretizations in the decision tree. Another interesting feature is that there lies a level of decision making between the two discrete approximations in the decision tree. This sets it apart from conventional discretized models since the accuracy in this case does not follow the rules and conventions that normal discrete models follow because of the decision between the two discrete approximations. The initial part of the work deals with varying the number of points chosen in the discrete model to test their accuracy against different correlation coefficients between the information and the actual values. The latter part deals more with comparing different methods of existing discretization methods and establishing conditions under which each is optimal. The problem is comprehensively dealt with in the cases of both a risk neutral and a risk averse decision maker.Show more