Parametric Estimating

Test Your Knowledge

Parametric Estimating Quiz

Instructions: Choose the best answer for each question.

1. What is the core principle behind Parametric Estimating?

a) Utilizing expert opinions to determine project costs. b) Breaking down projects into smaller tasks and estimating each one.

2. Which of the following is NOT a common parameter used in Oil & Gas Parametric Estimating?

a) Well depth b) Reservoir characteristics c) Project management experience

3. What is a key advantage of Parametric Estimating?

a) It eliminates the need for any historical data. b) It provides highly accurate estimates for all projects, regardless of complexity.

4. Which of the following is a potential challenge of Parametric Estimating?

a) Lack of available historical data b) Inability to handle projects with unique characteristics

5. What is a crucial step in ensuring effective Parametric Estimating?

a) Using a complex statistical analysis method. b) Choosing the most expensive historical data for analysis.

Parametric Estimating Exercise

Scenario: You are working on an oil & gas project involving drilling a new well. You have historical data from three similar projects:

| Project | Well Depth (meters) | Cost (Millions $) | |---|---|---| | A | 1500 | 10 | | B | 2000 | 14 | | C | 2500 | 18 |

Your project's well depth is 1800 meters. Using Parametric Estimating, calculate an initial cost estimate for your project.

Techniques

Chapter 1: Techniques in Parametric Estimating

This chapter delves into the various techniques employed in Parametric Estimating, providing a detailed understanding of their applications in Oil & Gas projects.

1.1. Linear Regression:

• Description: This technique establishes a linear relationship between project cost and one or more independent parameters. It assumes a straight-line correlation, ideal for projects with a consistent cost increase per unit change in parameter.
• Application: Estimating drilling costs based on well depth, pipeline costs based on length, or platform construction costs based on platform size.
• Example: A company uses historical data to establish a linear regression model for drilling costs. The model reveals that for every 100 meters increase in well depth, drilling costs increase by $50,000. This allows them to predict the drilling cost for a new well based on its depth.

1.2. Multiple Regression:

• Description: This technique expands upon linear regression by incorporating multiple independent variables. It allows for the analysis of more complex relationships where cost is influenced by a combination of parameters.
• Application: Estimating the cost of complex projects involving various factors like well depth, reservoir type, and production capacity.
• Example: An oil company uses multiple regression to model the cost of developing an offshore field. The model considers factors like water depth, distance from shore, reservoir complexity, and production volume.

1.3. Curve Fitting:

• Description: This technique uses non-linear equations to capture the relationship between cost and parameters. It is particularly useful for projects where cost increases non-linearly with a parameter.
• Application: Estimating the cost of complex reservoir stimulation treatments or specialized drilling operations.
• Example: A company uses a non-linear curve fitting model to estimate the cost of fracking operations. The model accounts for the increasing costs associated with increasing the number of fracking stages.

1.4. Statistical Indexes:

• Description: This technique uses pre-calculated indices to adjust historical costs for inflation and other economic factors. It helps maintain consistency and accuracy across different projects.
• Application: Adjusting historical costs for inflation or currency fluctuations.
• Example: An oil company uses the Producer Price Index (PPI) to adjust historical construction costs for inflation when estimating the cost of a new platform.

1.5. Expert Judgement:

• Description: This technique involves incorporating the expertise of experienced professionals in the field. Experts can provide valuable insights and adjustments to parametric estimates based on their knowledge of specific project complexities.
• Application: Refining estimates based on unique project challenges, unforeseen risks, or specific equipment requirements.
• Example: A team of engineers uses expert judgment to adjust a parametric estimate for a deepwater drilling project based on potential geological risks and challenging environmental conditions.

Chapter 2: Models in Parametric Estimating

This chapter explores various models used within the realm of Parametric Estimating for Oil & Gas projects, shedding light on their construction, application, and limitations.

2.1. Cost-Capacity Models:

• Description: These models relate project cost to its production capacity. They are useful for estimating costs associated with oil and gas extraction, processing, and transportation.
• Application: Estimating the cost of constructing a new refinery based on its processing capacity or a pipeline based on its throughput.
• Limitations: May not be suitable for projects with complex production processes or significant variations in technology.

2.2. Cost-Size Models:

• Description: These models relate project cost to its physical size or dimensions. They are applicable for estimating costs of infrastructure like platforms, pipelines, and facilities.
• Application: Estimating the cost of constructing a new offshore platform based on its area or a pipeline based on its length.
• Limitations: May not account for variations in construction complexity, materials, or location.

2.3. Cost-Complexity Models:

• Description: These models incorporate measures of project complexity to adjust base estimates. They acknowledge that more complex projects often require specialized equipment, more skilled personnel, and longer project durations.
• Application: Estimating the cost of drilling wells in complex geological formations or developing unconventional resources.
• Limitations: Defining and measuring complexity can be subjective and challenging.

2.4. Cost-Technology Models:

• Description: These models consider the specific technologies employed in a project, accounting for their impact on costs. They are particularly relevant for emerging technologies with varying cost profiles.
• Application: Estimating the cost of using new drilling technologies like horizontal drilling or hydraulic fracturing.
• Limitations: Requires up-to-date information on technology costs and their performance.

2.5. Cost-Risk Models:

• Description: These models incorporate risk assessment to adjust estimates for potential uncertainties and contingencies. They account for the probability and impact of potential risks.
• Application: Adjusting estimates for projects with high geological uncertainties, complex regulatory environments, or challenging operating conditions.
• Limitations: Requires accurate risk assessment and may lead to overly conservative estimates.

Chapter 3: Software for Parametric Estimating

This chapter examines various software tools used for implementing Parametric Estimating techniques in Oil & Gas projects, highlighting their key features and benefits.

3.1. Cost Estimating Software:

• Features: Cost estimating software offers modules for data management, statistical analysis, model building, and report generation. They enable users to create and manage parametric models, perform regression analysis, and generate cost estimates with detailed breakdowns.
• Examples: CostX, Primavera Cost Manager, AACE International's Cost Engineering Software
• Benefits: Streamlined workflow, increased accuracy, improved consistency, and time savings.

3.2. Data Analytics Platforms:

• Features: These platforms provide tools for data visualization, statistical analysis, machine learning, and predictive modeling. They can be used to analyze historical data, develop parametric models, and generate cost estimates.
• Examples: Power BI, Tableau, Alteryx, Python
• Benefits: Powerful data analysis capabilities, advanced modeling options, and flexibility.

3.3. Project Management Software:

• Features: Project management software often incorporates cost estimation tools, enabling integration of parametric estimates with project planning and scheduling.
• Examples: Microsoft Project, Primavera P6, Jira
• Benefits: Improved project control, resource allocation, and cost tracking.

3.4. Specialized Oil & Gas Estimating Tools:

• Features: Some software vendors offer specialized tools tailored for Oil & Gas cost estimating. These tools may include pre-built models for specific project types, industry-specific data sets, and features designed for regulatory compliance.
• Examples: WellCost, PetroCost, ProjectWise for Oil & Gas
• Benefits: Reduced time for model development, improved accuracy, and better alignment with industry standards.

3.5. Cloud-Based Platforms:

• Features: Cloud-based platforms offer accessibility, scalability, and collaboration features. They enable remote access, data sharing, and real-time updates.
• Examples: Amazon Web Services, Microsoft Azure, Google Cloud Platform
• Benefits: Enhanced collaboration, improved data security, and reduced infrastructure costs.

Chapter 4: Best Practices in Parametric Estimating

This chapter outlines best practices for implementing Parametric Estimating in Oil & Gas projects, ensuring accuracy, reliability, and consistency.

4.1. Data Quality:

• Importance: Accurate and complete historical data is crucial for building reliable parametric models.
• Practices:
  • Data Validation: Verify data for completeness, accuracy, and consistency.
  • Data Cleaning: Address inconsistencies, outliers, and missing values.
  • Data Standardization: Ensure data is presented in a consistent format.

4.2. Model Selection & Development:

• Practices:
  • Choose appropriate models: Select models aligned with the project complexity and available data.
  • Statistical Analysis: Validate model assumptions and assess model fit.
  • Sensitivity Analysis: Evaluate the impact of parameter variations on the estimate.

4.3. Validation & Refinement:

• Practices:
  • Compare estimates with historical data: Check model predictions against known project data.
  • Expert Review: Seek feedback from experienced professionals to validate and refine estimates.
  • Regular Updates: Update models periodically to reflect changing market conditions, technology advancements, and project experiences.

4.4. Communication & Transparency:

• Practices:
  • Document assumptions and methodology: Clearly outline the model's parameters, data sources, and limitations.
  • Communicate results effectively: Present estimates with confidence intervals and potential risks.
  • Transparency: Provide stakeholders with clear and concise explanations of the estimating process and its results.

4.5. Integration with Project Management:

• Practices:
  • Link estimates with project plans: Integrate parametric estimates into project schedules and budgets.
  • Track actual costs: Monitor project performance against estimates and identify deviations.
  • Update estimates as needed: Adjust estimates based on project changes, cost variations, and risk assessment.

Chapter 5: Case Studies in Parametric Estimating

This chapter presents real-world case studies of successful applications of Parametric Estimating in Oil & Gas projects, illustrating the technique's benefits and demonstrating its impact on project outcomes.

5.1. Offshore Platform Construction:

• Case: A major oil company used parametric estimating to estimate the cost of constructing a new offshore platform. They developed a model based on historical data from similar projects, considering factors like platform size, water depth, and construction complexity.
• Impact: The model provided an accurate estimate of project cost, enabling the company to plan and budget effectively. It also helped them identify potential risks and allocate resources efficiently.

5.2. Deepwater Well Drilling:

• Case: An oil exploration company employed parametric estimating to estimate the cost of drilling a deepwater well. They developed a model that factored in well depth, geological complexity, and specialized drilling equipment.
• Impact: The model provided a robust estimate, accounting for the challenges and uncertainties associated with deepwater drilling. It enabled the company to make informed decisions about project feasibility and resource allocation.

5.3. Pipeline Construction:

• Case: A pipeline construction company used parametric estimating to estimate the cost of constructing a new pipeline. They developed a model that incorporated pipeline length, terrain conditions, and environmental regulations.
• Impact: The model provided a reliable cost estimate, enabling the company to secure financing, bid on projects, and manage resources effectively.

5.4. Oil Sands Extraction:

• Case: An oil sands extraction company used parametric estimating to estimate the cost of expanding their mining operations. They developed a model that considered factors like ore grade, extraction technology, and infrastructure requirements.
• Impact: The model provided a comprehensive cost estimate, supporting investment decisions, resource allocation, and project planning.

5.5. Unconventional Gas Development:

• Case: An energy company used parametric estimating to estimate the cost of developing a shale gas play. They developed a model that incorporated factors like well density, fracking intensity, and gas production rates.
• Impact: The model provided a robust cost estimate, enabling the company to evaluate the economic viability of the project and make informed decisions about resource allocation.

Conclusion:

These case studies demonstrate the effectiveness of Parametric Estimating in various Oil & Gas projects. The technique's ability to leverage historical data, incorporate statistical relationships, and account for project-specific variables enables accurate cost estimations, promoting efficient planning, resource allocation, and risk management. By adhering to best practices and selecting appropriate models, companies can utilize Parametric Estimating as a valuable tool for successful project execution in the dynamic Oil & Gas industry.