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.

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.

One of the issues with multilateral wells is determining the contribution of each lateral to the total production that is measured at the surface. Also, if water is detected at the surface or if the multilateral well performance declines, then it is difficult to identify which lateral or laterals are causing the production decline. One way to estimate the contribution from each lateral is to run production Logging Tools (PLT). Unfortunately, PLT jobs are expensive, time-consuming, labor-intensive and involve operational risks. An alternative way to measure the production from each lateral is to use Distributed Temperature Sensing (DTS) technology. Recent advances in DTS technology enable measuring the temperature profile in horizontal wells with high precision and resolution. The changes in the temperature profile are successfully used to calculate the production profile in horizontal wells. In this research, we develop a computer program that uses a multilateral well model to calculate the pressure and temperature profile in the motherbore. The results help understand the temperature and pressure behaviors in multilateral wells that are crucial in designing and optimizing DTS installations. Also, this model can be coupled with an inversion model that can use the measured temperature and pressure profile to calculate the production from each lateral. Our model shows that changing the permeability or the water cut produced from one lateral results in a clear signature in the motherbore temperature profile that can be measured with DTS technology. However, varying the length of one of the lateral did not seem to impact the temperature profile in the motherbore. For future work, this research recommends developing a numerical reservoir model that would enable studying the effect of lateral inference and reservoir heterogeneity. Also recommended is developing an inversion model that can be used to validate our model using field data.