In this study, a three-dimensional, three-phase dynamic simulation model based on geological investigations of Korolev oilfield in Kazakhstan was used as a development planning tool in order to improve performance of three new wells. The model, developed previously by means of a seismic study, well log and core data, incorporating with characteristics of oilfield productivity, properties of reservoir, liquids and gases that are saturating the hydrocarbon-bearing horizon can be used to calculate development parameters for Korolev field, including production well locations, drilling schedules, and to facilitate both long-term and short-term forecasting for the purposes of optimizing the hydrocarbon recovery from the field. The objective of this work is to assess the impact of adding 3 extra producing wells and find ways to optimize cumulative production with the least impact on the existing development plan by means of deeper understanding subsurface dynamic processes of the Korolev field. The challenge is a high degree of connectivity between wells in the productive formation throughout the field so that any change of production parameters affects the whole field&rsquo / s cumulative production. Trying to find a solution to optimum production of the reservoir forecast studies were carried out, the impact of each new well on development parameters was defined, sub-surface processes changes due to extra producers lead-in were explained and as a result of this thesis two optimization models were proposed, one of which will bring nearly 9.7 million barrels more oil.

Exploration and production (E&P) involves the upstream activities from looking for promising reservoirs to extracting oil and selling it to downstream companies. E&P is the most profitable business in the oil industry. However, it is also the most capital-intensive and risky. Hence, the proper assessment of E&P projects with effective management of uncertainties is crucial to the success of any upstream business. This dissertation is concentrated on developing portfolio optimization models to manage E&P projects. The idea is not new, but it has been mostly restricted to the conceptual level due to the inherent complications to capture interactions among projects. We disentangle the complications by modeling the project portfolio optimization problem as multistage stochastic programs with mixed integer programming (MIP) techniques. Due to the disparate nature of uncertainties, we separately consider explored and unexplored oil fields. We model portfolios of real options and portfolios of decision trees for the two cases, respectively. The resulting project portfolio models provide rigorous and consistent treatments to optimally balance the total rewards and the overall risk. For explored oil fields, oil price fluctuations dominate the geologic risk. The field development process hence can be modeled and assessed as sequentially compounded options with our optimization based option pricing models. We can further model the portfolio of real options to solve the dynamic capital budgeting problem for oil projects. For unexplored oil fields, the geologic risk plays the dominating role to determine how a field is optimally explored and developed. We can model the E&P process as a decision tree in the form of an optimization model with MIP techniques. By applying the inventory-style budget constraints, we can pool multiple project-specific decision trees to get the multistage E&P project portfolio optimization (MEPPO) model. The resulting large scale MILP is efficiently solved by a decomposition-based primal heuristic algorithm. The MEPPO model requires a scenario tree to approximate the stochastic process of the geologic parameters. We apply statistical learning, Monte Carlo simulation, and scenario reduction methods to generate the scenario tree, in which prior beliefs can be progressively refined with new information. / text