In the oil and gas industry today, continuous wellbore data can be obtained with high precision. This accurate and reliable downhole data acquisition is made possible by advancements in permanent monitoring systems such as downhole pressure and temperature gauges and fiber optic sensors. The monitoring instruments are increasingly incorporated as part of the intelligent completion in oil wells where they provide bottomhole temperature, pressure and sometimes volumetric flow rate along the wellbore - offering the promise of revolutionary changes in the way these wells are operated. However, to fully realize the value of these intelligent completions, there is a need for a systematic data analysis process to interpret accurately and efficiently the raw data being acquired. This process will improve our understanding of the reservoir and production conditions and enable us make decisions for well control and well performance optimization. In this study, we evaluated the practical application of an interpretation model, developed in a previous research work, to field data. To achieve the objectives, we developed a simple and detailed analysis procedure and built Excel user interface for data entry, data update and data output, including diagnostic charts and graphs. By applying our interpretation procedure to the acquired field data we predicted temperature and pressure along the wellbore. Based on the predicted data, we used an inversion method to infer the flow profile - demonstrating how the monitored raw downhole temperature and pressure can be converted into useful knowledge of the phase flow profiles and fluid entry along the wellbore. Finally, we illustrated the sensitivity of reservoir parameters on accuracy of interpretation, and generated practical guidelines on how to initialize the inverse process. Field production logging data were used for validation and application purposes. From the analysis, we obtained the production profile along the wellbore; the fluid entry location i.e. the productive and non-productive locations along the wellbore; and identified the fluid type i.e. gas or water being produced along the wellbore. These results show that temperature and pressure profiles could provide sufficient information for fluid identity and inflow distribution in gas wells.

The oil and gas industry, more than at any time in the past, is highly affected by technological advancements, new products, drilling and completion techniques, capital expenditures (CAPEX), operating expenditures (OPEX), risk/uncertainty, and geopolitics. Therefore, to make a decision in the upstream business, projects require a thorough understanding of the factors and conditions affecting them in order to systematically analyze, evaluate and select the best choice among all possible alternatives. The objective of this study is to develop a methodology to assist engineers in the decision making process of maximizing access to reserves. The process encompasses technical, economic and risk analysis of various alternatives in the completion of a well (vertical, horizontal or multilateral) by using a well performance model for technical evaluation and a deterministic analysis for economic and risk assessment. In the technical analysis of the decision making process, the flow rate for a defined reservoir is estimated by using a pseudo-steady state flow regime assumption. The economic analysis departs from the utilization of the flow rate data which assumes a certain pressure decline. The financial cash flow (FCF) is generated for the purpose of measuring the economic worth of investment proposals. A deterministic decision tree is then used to represent the risks inherent due to geological uncertainty, reservoir engineering, drilling, and completion for a particular well. The net present value (NPV) is utilized as the base economic indicator. By selecting a type of well that maximizes the expected monetary value (EMV) in a decision tree, we can make the best decision based on a thorough understanding of the prospect. The method introduced in this study emphasizes the importance of a multi-discipline concept in drilling, completion and operation of multilateral wells.

We have studied the electronic properties of InxGa1-xAs/ In0.52Al0.48As quantum wells by using Shubnickove-de Hass (SdH) measurement. The indium composition (x) of well layers was varied from 0.5 to 0.56 whit different structures, such as sample A is simply ¡§In0.53Ga0.47As¡¨, sample B is a step-well like¡§In0.56Ga0.44As/In0.53Ga0.47As/In0.5Ga0.47As¡¨,sample C is linearly graded well is a opposite way¡§In0.56Ga0.44As down to In0.5Ga0.5As, and sample D is linearly graded well ¡§In0.5Ga0.5As up to In0.56Ga0.44As.¡¨ It was found that the two SdH oscillations beat each other due to the population of the lowest two subbands in these samples. In order to investigate the electronic properties of the two subbands, we have done the Ven der Pauw Hall measurement . From SdH and Hall measurement, we are able to determine the individual mobility and carrier concentrations for two-subband-populated samples.

Intelligent well completions are increasingly being used in horizontal, multilateral, and multi-branching wells. Such completions are equipped with permanent sensors to measure temperature and pressure profiles, which must then be interpreted to determine the inflow profiles of the various phases produced that are needed to characterize the well??s performance. Distributed temperature measurements, using fiber optics in particular, are becoming increasingly more often applied. The value of an intelligent completion hinges on our capability to extract such inflow profiles or, at a minimum, to locate the entry locations of undesirable water or gas entries. In this research, a model of temperature behavior in multilateral wells was developed. The model predicts the temperature profiles in the build sections connecting the laterals to one another or to a main wellbore, thus accounting for the changing well angle relative to the temperature profile in the earth. In addition, energy balance equations applied at each junction predict the effects of mixing on the temperature above each junction. The multilateral wellbore temperature model was applied to a wide range of cases, in order to determine the conditions for which intelligent completions would be most useful. Parameters that were varied for this experiment included fluid thermal properties, absolute values of temperature and pressure, geothermal gradients, flow rates from each lateral, and the trajectories of each build section. From this parametric study, guidelines for an optimal application of intelligent well completion are represented.

Predicting the performance of wells in compartmentalized reservoirs can be quite challenging to most conventional reservoir engineering tools. The purpose of this research is to develop a Compartmentalized Gas Depletion Model that applies not only to conventional consolidated reservoirs (with constant formation compressibility) but also to unconsolidated reservoirs (with variable formation compressibility) by including geomechanics, permeability deterioration and compartmentalization to estimate the OGIP and performance characteristics of each compartment in such reservoirs given production data. A geomechanics model was developed using available correlation in the industry to estimate variable pore volume compressibility, reservoir compaction and permeability reduction. The geomechanics calculations were combined with gas material balance equation and pseudo-steady state equation and the model was used to predict well performance. Simulated production data from a conventional gas Simulator was used for consolidated reservoir cases while synthetic data (generated by the model using known parameters) was used for unconsolidated reservoir cases. In both cases, the Compartmentalized Depletion Model was used to analyze data, and estimate the OGIP and Jg of each compartment in a compartmentalized gas reservoir and predict the subsequent reservoir performance. The analysis was done by history-matching gas rate with the model using an optimization technique. The model gave satisfactory results with both consolidated and unconsolidated reservoirs for single and multiple reservoir layers. It was demonstrated that for unconsolidated reservoirs, reduction in permeability and reservoir compaction could be very significant especially for unconsolidated gas reservoirs with large pay thickness and large depletion pressure.

Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 181-194). Available also in a digital version from Dissertation Abstracts.

Thesis (Ph. D.)--Ohio State University, 2004. / Title from first page of PDF file. Document formatted into pages; contains xv, 150 p.; also includes graphics (some col.). Includes abstract and vita. Advisor: Jonathan P. Pelz, Dept. of Physics. Includes bibliographical references (p. 144-150).

Thesis (Ph. D.)--University of Rhode Island, 2003. / Typescript. Includes bibliographical references (leaves 109-123).

Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, June 2004. / Thesis advisor(s): Nancy M. Haegel, John Powers. Includes bibliographical references (p. 53-54). Also available online.

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Supervisor: Russell D. Dupuis. Vita. Includes bibliographical references. Available also from UMI Company.