An Integrated Approach for Optimizing Upstream Petroleum Investment in Representative Oil and Gas Fields: A Case Study

Introduction

The upstream petroleum industry is a complex and dynamic field that requires strategic decision-making to maximize returns on investment. In this article, we present an integrated approach using Analytic Hierarchy Process (AHP) and fuzzy entropy-TOPSIS methods to optimize upstream petroleum investment in representative oil and gas fields. We will discuss the application of these methods in a case study, highlighting the benefits of using a comprehensive decision-making framework for investment optimization in the petroleum industry.

Background

In the upstream petroleum industry, decision-making for investment in oil and gas fields is influenced by numerous factors such as geological, technological, economic, and environmental considerations. The complexity of these factors makes it challenging for decision-makers to effectively assess and prioritize investment opportunities. Traditional decision-making approaches may not adequately capture the complexity and uncertainty associated with these factors, leading to suboptimal investment decisions and missed opportunities for maximizing returns.

Analytic Hierarchy Process (AHP)

AHP is a widely used multi-criteria decision-making method that allows decision-makers to systematically evaluate and prioritize alternatives based on multiple criteria. The AHP method involves structuring the decision problem into a hierarchical model, where decision criteria are organized into a top-level goal, sub-goals, and alternatives. Pairwise comparisons are then used to assess the relative importance of criteria and alternatives, leading to the derivation of decision weights and the overall ranking of alternatives.

In the context of upstream petroleum investment, AHP can be applied to assess the importance of criteria such as reservoir potential, production costs, environmental impact, and market demand. By leveraging AHP, decision-makers can gain insights into the relative significance of these criteria and make informed investment decisions that align with overall strategic objectives.

Fuzzy Entropy-TOPSIS Method

The fuzzy entropy-TOPSIS method is a hybrid approach that combines fuzzy entropy theory with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Fuzzy entropy theory is used to capture and quantify the uncertainty and vagueness associated with decision criteria, while TOPSIS is utilized to rank alternatives based on their proximity to the ideal solution and furthest from the negative ideal solution.

In the context of upstream petroleum investment, the fuzzy entropy-TOPSIS method can account for the uncertainty and imprecision inherent in decision criteria such as geological reserves, production potential, and economic feasibility. By incorporating fuzzy entropy, decision-makers can better handle the ambiguity and variability of these criteria, leading to more robust and realistic investment prioritization outcomes.

Case Study: Optimizing Upstream Petroleum Investment

In a representative oil and gas field, a petroleum company is evaluating multiple investment opportunities to maximize returns and achieve long-term strategic goals. The decision-making team is tasked with identifying the most promising investment options based on a set of criteria including geological reserves, production potential, production costs, environmental impact, and market demand. To address the complexity and uncertainty of the decision problem, the integrated approach of AHP and fuzzy entropy-TOPSIS is applied to optimize upstream petroleum investment.

AHP Application

The decision team begins by structuring the decision problem into a hierarchical model, with the top-level goal of maximizing returns on investment. Sub-goals are defined to represent the key criteria for investment assessment, and potential investment opportunities are identified as alternatives. Pairwise comparisons are conducted to elicit the relative importance of criteria and alternatives, resulting in the derivation of decision weights and the overall ranking of investment opportunities.

Using AHP, the decision team gains valuable insights into the relative significance of criteria such as geological reserves, production potential, production costs, environmental impact, and market demand. By leveraging the derived decision weights, the team is able to prioritize investment opportunities based on their alignment with the strategic goal of maximizing returns.

Fuzzy Entropy-TOPSIS Application

Building on the insights from the AHP analysis, the decision team applies the fuzzy entropy-TOPSIS method to further assess and rank the investment opportunities. Fuzzy entropy theory is used to capture the uncertainty and variability of decision criteria, allowing for a more realistic representation of the decision problem. The TOPSIS method is then employed to rank the investment opportunities based on their proximity to the ideal solution and furthest from the negative ideal solution.

Incorporating fuzzy entropy allows the decision team to account for the ambiguity and vagueness associated with criteria such as geological reserves, production potential, and economic feasibility. By utilizing the fuzzy entropy-TOPSIS method, the team is able to generate a robust ranking of investment opportunities that considers the complexity and uncertainty of the decision problem.

Benefits of Integrated Approach

The integrated approach of AHP and fuzzy entropy-TOPSIS for optimizing upstream petroleum investment offers several key benefits:

Comprehensive Decision-Making: The hierarchical modeling of AHP allows decision-makers to systematically assess and prioritize investment opportunities based on multiple criteria, leading to a more holistic and informed decision-making process.
Uncertainty Handling: Fuzzy entropy theory enables decision-makers to capture and quantify the uncertainty and imprecision associated with decision criteria, resulting in more realistic and robust investment prioritization outcomes.
Enhanced Transparency: The structured nature of the AHP and fuzzy entropy-TOPSIS methods enhances the transparency of the decision-making process, allowing stakeholders to gain a clear understanding of the rationale behind investment prioritization.

Conclusion

In the complex and dynamic field of upstream petroleum investment, the integrated approach of AHP and fuzzy entropy-TOPSIS offers a comprehensive decision-making framework for optimizing investment in oil and gas fields. By leveraging AHP for systematic evaluation and prioritization of investment opportunities, and integrating fuzzy entropy-TOPSIS to account for uncertainty and ambiguity, decision-makers can make informed and strategic investment decisions that align with long-term objectives. The case study presented in this article demonstrates the practical application and benefits of this integrated approach, illustrating its potential to enhance investment optimization in the petroleum industry.