Should-Cost Estimates

Test Your Knowledge

Should-Cost Estimates Quiz

Instructions: Choose the best answer for each question.

1. What is the primary purpose of a should-cost estimate?

a) To predict future oil prices. b) To assess the reasonableness of proposed costs from contractors. c) To determine the feasibility of new oil and gas technologies. d) To track the progress of ongoing oil and gas projects.

2. Which of the following is NOT a key component of a should-cost estimate?

a) Direct Costs b) Indirect Costs c) Marketing Expenses d) Contingency

3. How can should-cost estimates help oil and gas companies strengthen their negotiation positions?

a) By providing a basis for comparison with competitor bids. b) By outlining the company's internal cost structure to contractors. c) By demonstrating the company's commitment to sustainable practices. d) By offering a detailed breakdown of project costs to contractors.

4. Which of the following best describes the importance of historical data in should-cost estimates?

a) It helps predict future oil prices. b) It establishes a baseline for cost estimation. c) It ensures compliance with environmental regulations. d) It tracks the progress of ongoing oil and gas projects.

5. Why are regular reviews and updates crucial for should-cost estimates?

a) To meet regulatory requirements. b) To reflect changing market dynamics and project progress. c) To track the performance of contractors. d) To predict future oil prices.

Should-Cost Estimates Exercise

Scenario:

You are a cost engineer working for an oil and gas company. Your company is considering bidding on a project to develop a new offshore oil platform. You are tasked with creating a preliminary should-cost estimate to assess the project's feasibility.

Information provided:

• Direct Costs:
  • Labor: $50 million
  • Materials: $30 million
  • Equipment: $20 million
• Indirect Costs:
  • Overhead: $10 million
• Profit Margin: 10% of total direct and indirect costs
• Contingency: 5% of total direct and indirect costs

Tasks:

1. Calculate the total direct and indirect costs.
2. Calculate the profit margin.
3. Calculate the contingency.
4. Calculate the total estimated project cost.

Techniques

Should-Cost Estimates in Oil & Gas: A Comprehensive Guide

This guide expands on the introduction to Should-Cost Estimates in the oil & gas industry, breaking down the topic into key chapters for clarity and understanding.

Chapter 1: Techniques

Should-cost estimation employs various techniques to arrive at a realistic cost benchmark. These techniques often involve a combination of approaches, depending on the complexity of the project and data availability.

1.1 Parametric Estimating: This technique uses historical data and statistical relationships to predict costs based on key project parameters. For instance, the cost of a pipeline might be estimated based on its length, diameter, and terrain. This method is efficient for preliminary estimations but requires a robust database of past projects.

1.2 Bottom-Up Estimating: This is a detailed approach where the costs of individual components and activities are estimated and aggregated. It requires a comprehensive work breakdown structure (WBS) and detailed cost breakdowns for each work package. This method is more accurate but time-consuming.

1.3 Top-Down Estimating: This approach starts with a high-level cost estimate, often derived from similar projects, and then progressively breaks it down into smaller components. While faster than bottom-up, it’s less accurate and relies heavily on the accuracy of the initial estimate.

1.4 Activity-Based Costing (ABC): ABC identifies the activities involved in a project and assigns costs to each activity based on resource consumption. This approach helps to identify areas of cost inefficiency and provides a more detailed understanding of the cost drivers.

1.5 Engineering-Based Estimating: This technique uses detailed engineering drawings and specifications to estimate material quantities and labor requirements. It is highly accurate but requires significant engineering input and is best suited for projects in later stages of development.

1.6 Hybrid Approaches: In practice, a combination of these techniques is often used. For example, a parametric estimate might be refined using bottom-up costing for critical components or activities. The choice of technique depends on the project phase, available data, and desired level of accuracy.

Chapter 2: Models

Developing accurate should-cost models requires careful consideration of various factors and the selection of appropriate models.

2.1 Cost Breakdown Structure (CBS): A detailed hierarchical breakdown of all costs associated with the project, reflecting the project's structure. This ensures all costs are captured and categorized logically.

2.2 Cost Drivers: Identifying and quantifying the key factors influencing project costs (e.g., material prices, labor rates, equipment rental costs, weather conditions, regulatory compliance). Understanding these drivers is crucial for accurate estimation and sensitivity analysis.

2.3 Risk Assessment and Contingency Planning: Incorporating potential risks and uncertainties into the model. This might involve probabilistic modeling to estimate the likelihood and impact of various risks and allocating contingency reserves accordingly. Monte Carlo simulations are commonly used for this purpose.

2.4 Learning Curve Analysis: Accounting for efficiency improvements that can occur as project teams gain experience. This is particularly relevant for repetitive tasks or projects with similar characteristics.

2.5 Inflation and Currency Fluctuations: Adjusting cost estimates to account for inflation and potential currency exchange rate fluctuations, particularly for international projects.

2.6 Data Sources: Defining the data sources used for the model (historical project data, market surveys, supplier quotations, industry benchmarks). Data quality and reliability are critical for model accuracy.

Chapter 3: Software

Several software tools can streamline the should-cost estimation process.

3.1 Spreadsheet Software (Excel): While basic, spreadsheets can be used for simple projects. However, for complex projects, dedicated software is recommended.

3.2 Cost Estimating Software: Specialized software packages, such as those offered by Primavera, CostX, and other industry-specific solutions, offer advanced features for cost modeling, risk analysis, and reporting. These often integrate with project management software.

3.3 Data Analytics Platforms: Platforms like Power BI or Tableau can help visualize and analyze large datasets used for should-cost estimation, providing insights into cost drivers and trends.

3.4 Cloud-Based Solutions: Cloud-based platforms enhance collaboration and data accessibility, facilitating efficient cost estimation among distributed teams.

Chapter 4: Best Practices

Implementing best practices ensures accurate and reliable should-cost estimates.

4.1 Experienced Team: Assemble a team with expertise in cost engineering, oil & gas operations, and relevant technologies.

4.2 Data Integrity: Utilize reliable and validated historical data, ensuring data consistency and accuracy. Regular data audits are essential.

4.3 Transparency and Documentation: Maintain detailed documentation of the estimation process, assumptions, and data sources to ensure transparency and facilitate audits.

4.4 Regular Review and Updates: Periodically review and update the should-cost estimate to reflect changing market conditions, project progress, and new information.

4.5 Sensitivity Analysis: Conduct sensitivity analysis to assess the impact of variations in key parameters on the overall cost estimate.

4.6 Independent Verification: Consider using an independent third party to review and validate the should-cost estimate for objectivity.

Chapter 5: Case Studies

(This chapter would include specific examples of should-cost estimates applied in real-world oil & gas projects. Each case study would detail the project, the techniques employed, the results achieved, and any lessons learned. Examples could include offshore platform construction, pipeline projects, or refinery upgrades. Due to confidentiality, real-world examples would need to be anonymized or hypothetical.)

For instance, a hypothetical case study could describe how a bottom-up estimation approach combined with parametric modeling for certain components helped a company successfully negotiate a lower contract price for a subsea pipeline installation, resulting in significant cost savings. Another case study might illustrate how the use of a dedicated cost estimating software improved the accuracy and efficiency of the estimation process for an offshore platform construction project. These case studies would showcase the practical application of the concepts discussed in previous chapters.