Order of Magnitude Estimate

Test Your Knowledge

Quiz: Order of Magnitude Estimates in Oil & Gas

Instructions: Choose the best answer for each question.

1. What is the primary purpose of an Order of Magnitude Estimate (OME)?

(a) To determine the exact cost of a project. (b) To provide a detailed breakdown of project expenses. (c) To quickly assess the potential cost of a project in its early stages. (d) To evaluate the environmental impact of a project.

2. What is the typical accuracy range of an OME?

(a) -10% to +10% (b) -25% to +25% (c) -50% to +100% (d) -100% to +100%

3. When is an OME most commonly used?

(a) During detailed engineering studies. (b) After project completion. (c) During the initial stages of project development. (d) To estimate project risks.

4. Which of the following is NOT a key consideration for using an OME?

(a) The project's environmental impact. (b) Limited information and assumptions. (c) Potential risk factors. (d) Iterative refinement of the estimate.

5. What is the main advantage of using an OME?

(a) It provides a highly accurate cost estimate. (b) It is a time-consuming and detailed process. (c) It is a quick and cost-effective way to assess project feasibility. (d) It requires extensive data and analysis.

Exercise: Order of Magnitude Estimate

Scenario:

A new oil exploration project is being considered in a remote location. Initial estimates indicate a potential oil reserve of 10 million barrels. Based on historical data and industry benchmarks, the following assumptions are made:

• Drilling Cost: $10 million per well
• Production Cost: $5 per barrel
• Transportation Cost: $2 per barrel
• Number of Wells: 5

Task:

Calculate an order of magnitude estimate (OME) for the total cost of the oil exploration project, using the provided information.

Techniques

Order of Magnitude Estimate: A Crucial Tool in Oil & Gas Costing

Chapter 1: Techniques

Order of Magnitude Estimates (OMEs) rely on various techniques to arrive at a reasonable approximation of project costs. These techniques are generally simplified and rely heavily on readily available information, rather than detailed engineering drawings or specifications. Common techniques include:

• Top-Down Approach: This approach starts with the overall project scope and breaks it down into major components. Cost estimates for each component are then developed using historical data, industry benchmarks, or similar projects. These individual component estimates are then aggregated to arrive at the total OME. This is particularly useful in early stages where detailed information is scarce.

• Bottom-Up Approach (Simplified): While a full bottom-up estimate requires detailed breakdown, a simplified version can be used for OMEs. This involves identifying key cost drivers and estimating their cost based on limited data. This requires strong experience and judgment to select the right cost drivers and apply appropriate cost factors. It's less suitable than the top-down approach in the very early stages.

• Parametric Estimating: This method uses statistical relationships between project characteristics (e.g., size, capacity, location) and cost. Pre-established cost models or equations are applied to the project parameters to generate a cost estimate. This approach requires access to reliable parametric cost databases specific to the oil and gas industry.

• Analogous Estimating: This technique compares the project to similar past projects. The cost of the similar project is adjusted based on differences in scope, location, technology, and other relevant factors. The accuracy depends heavily on the similarity between the projects being compared.

• Expert Judgment: This is crucial in all OME techniques. Experienced engineers and cost estimators use their knowledge and experience to adjust the estimates obtained through other methods, accounting for project-specific risks and uncertainties.

Chapter 2: Models

Several models underpin the various OME techniques. While there isn't one universal model, the underlying principles are similar. Key elements often incorporated are:

• Cost Drivers: These are factors that significantly impact the overall project cost. In oil and gas projects, key cost drivers may include well depth, reservoir type, location (onshore vs. offshore), technology used, regulatory requirements, and labor costs.

• Scaling Factors: These are used to adjust the cost estimates based on the size or complexity of the project. For example, a larger offshore platform will cost more than a smaller one, and this difference can be represented through a scaling factor.

• Contingency Factors: These are added to account for uncertainties and unforeseen events that may arise during the project. The contingency factor is usually expressed as a percentage of the estimated cost. It's higher for OMEs than for more detailed estimates.

• Inflation Factors: The future cost of materials, labor, and services is often accounted for by including inflation factors. These factors are applied to the base cost estimate to arrive at a future value.

Chapter 3: Software

While specialized software isn't strictly necessary for performing OMEs (basic spreadsheets are often sufficient), certain software can aid in the process:

• Spreadsheet Software (Excel, Google Sheets): These are commonly used for organizing data, performing calculations, and creating charts to visualize the cost estimates.

• Cost Estimating Software: Specialized software packages offer more advanced features, such as parametric estimating capabilities, database access, and risk analysis tools. Examples include AACE International's software solutions and others specific to the oil and gas sector.

• Project Management Software: Software such as MS Project or Primavera P6 can assist in managing the overall project schedule and costs, which indirectly supports the OME process.

Chapter 4: Best Practices

Several best practices enhance the reliability and value of OMEs:

• Clearly Define the Scope: A well-defined scope is fundamental. Ambiguity leads to inaccurate estimates.

• Use Reliable Data: Rely on credible historical data, industry benchmarks, and relevant information.

• Identify and Quantify Risks: Explicitly address uncertainties and potential cost overruns. This requires experience and understanding of the oil and gas industry’s specific risks.

• Document Assumptions: All assumptions made during the estimation process should be clearly documented.

• Iterative Refinement: OMEs should be revisited and refined as more information becomes available. Treat it as a living document.

• Independent Verification: Having a second independent team review the OME can identify potential biases or errors.

• Transparency and Communication: The OME’s limitations and assumptions should be clearly communicated to stakeholders.

Chapter 5: Case Studies

(This section would contain real-world examples of how OMEs were used in the oil and gas industry. Each case study would detail the project, the OME process used, the resulting estimate, and a comparison with the final cost if available. Due to the confidential nature of such data, hypothetical but realistic examples are provided below):

Case Study 1: Offshore Platform Development: An OME for a new offshore oil platform was conducted using the top-down approach. Historical data on similar platforms and industry benchmarks were used to estimate the cost of major components (drilling, platform construction, equipment, installation). The initial OME indicated a cost range of $1.5 Billion to $3 Billion. Further detailed engineering revealed the actual cost to be $2.2 Billion, within the OME range.

Case Study 2: Onshore Pipeline Construction: A parametric model was used to estimate the cost of a new onshore gas pipeline. The model considered the pipeline length, diameter, terrain, and environmental factors. The OME indicated a cost of $500 Million to $700 Million. This estimate proved to be remarkably accurate, with the final cost settling at $620 Million. However, this accuracy was largely due to well-defined terrain and limited regulatory challenges.

(Note: Real-world case studies would need to be sourced from publicly available information or with the permission of the involved companies, due to confidentiality concerns.)