GERT

Test Your Knowledge

GERT Quiz:

Instructions: Choose the best answer for each question.

1. What is the primary difference between GERT and traditional project management tools like PERT and CPM?

a) GERT focuses solely on project scheduling. b) GERT accounts for probabilistic events and uncertainties. c) GERT is only suitable for small projects. d) GERT relies on deterministic timelines.

2. What feature of GERT allows for the representation of repeated activities based on previous outcomes?

a) Network Diagram b) Conditional Logic c) Simulation d) Looping Activities

3. Which of the following is NOT a benefit of using GERT in Oil & Gas projects?

a) Enhanced risk management. b) Improved decision-making. c) Reduced project costs. d) Increased project success.

4. How does GERT facilitate risk assessment in Oil & Gas projects?

a) By identifying potential risks and their impact. b) By providing a framework for developing mitigation strategies. c) By simulating project scenarios under different conditions. d) All of the above.

5. Which Oil & Gas application of GERT helps in evaluating the economic viability of a project?

a) Exploration and Production b) Project Planning and Scheduling c) Capital Budgeting d) Risk Assessment and Management

GERT Exercise:

Scenario: An oil company is planning to drill an exploratory well. The project involves several steps, including:

1. Site Selection: This activity has a 70% chance of success, and a 30% chance of failure, requiring a new site selection.
2. Drilling: If site selection is successful, drilling has an 80% chance of success and a 20% chance of encountering problems requiring a costly rework.
3. Production: If drilling is successful, production has a 90% chance of being profitable and a 10% chance of being unprofitable.

Task:

1. Create a simple GERT network diagram representing this project.
2. Identify the critical path and the most likely outcomes based on the probabilities provided.
3. Discuss the potential risks associated with this project and how GERT can help manage them.

Techniques

GERT: A Powerful Tool for Project Management in Oil & Gas

Chapter 1: Techniques

GERT, or Graphical Evaluation and Review Technique, leverages a network diagram to represent project activities, their dependencies, and potential outcomes. Unlike deterministic methods like CPM (Critical Path Method), GERT embraces uncertainty. Its core techniques revolve around:

• Node Representation: Nodes in the GERT network represent events or activities. They can be classified as:

  • Start Node: The beginning of the project.
  • End Node: The completion of the project or a specific branch.
  • Event Node: Represents a point in time where an event occurs, possibly triggering multiple subsequent activities.
  • Activity Node: Represents a task or activity with a defined duration.

• Branch Representation: Branches connecting nodes depict the flow of activities. They incorporate probabilities and conditional logic:

  • Probabilistic Branches: Represent the likelihood of different outcomes following an event. The sum of probabilities on branches leaving a single node must equal one.
  • Conditional Branches: Indicate that an activity only proceeds if a specific condition is met.

• Looping: GERT uniquely handles looping activities. This iterative capability is crucial in oil & gas projects involving repetitive tasks like well testing or pipeline inspections. Loops are represented by branches returning to previous nodes.

• Simulation: The power of GERT lies in its simulation capabilities. By inputting activity durations (often probabilistic distributions), branch probabilities, and conditional logic, the model simulates project progression under various scenarios, producing outputs like:

  • Project Completion Time Distribution: Shows the probability of finishing within a given timeframe.
  • Critical Path Probabilities: Identifies the most likely critical path(s).
  • Resource Utilization Analysis: Helps optimize resource allocation.

Chapter 2: Models

Several models can be built within the GERT framework, depending on project complexity and the level of detail required. These might include:

• Simple GERT Networks: These represent projects with a straightforward sequence of activities and minimal uncertainties. They're suitable for preliminary planning or small-scale projects.

• Complex GERT Networks: These incorporate significant uncertainties, probabilistic branching, looping, and conditional logic. They're ideal for large-scale, complex oil & gas projects where risk management is paramount.

• Stochastic GERT Networks: These explicitly incorporate probabilistic distributions for activity durations, making the model capable of handling uncertainty inherent in many oil & gas operations, such as drilling success rates or reservoir estimations.

• Markov Chain Models within GERT: These can be used to model situations with repetitive activities and dependencies across multiple iterations.

The selection of the appropriate model depends on project characteristics and the level of detail needed for decision-making.

Chapter 3: Software

While GERT's core concepts are relatively straightforward, manual construction and analysis of complex networks are cumbersome. Dedicated software packages or general-purpose simulation tools are essential for efficient implementation:

• Specialized GERT Software: Historically, specialized GERT software packages existed. However, these have largely been replaced by more versatile options.

• General-Purpose Simulation Software: Software such as Arena, AnyLogic, and Simul8 offer the flexibility to model GERT networks and conduct simulations. These packages provide features for defining nodes, branches, probabilistic distributions, and analyzing simulation results. Users typically define the network visually, specify parameters, run simulations, and interpret the results.

• Custom Programming: For highly specialized needs or integration with other systems, custom programming using languages like Python with appropriate libraries can be employed.

Chapter 4: Best Practices

Effective implementation of GERT requires adherence to certain best practices:

• Clearly Defined Scope: Before model creation, the project scope, objectives, and key activities must be precisely defined.

• Accurate Data Collection: Accurate estimates of activity durations and probabilities are crucial for reliable simulation results. Data should be sourced from historical data, expert judgments, and relevant literature.

• Model Validation: The GERT model should be validated against historical data or expert opinions to ensure its accuracy and reliability.

• Sensitivity Analysis: Conducting sensitivity analysis helps to understand the impact of uncertainties on project outcomes. This informs risk management strategies.

• Iteration and Refinement: The GERT model should be iteratively refined as more information becomes available and uncertainties are reduced.

• Collaboration and Communication: Effective communication and collaboration among project stakeholders are vital for successful GERT implementation.

Chapter 5: Case Studies

(Note: Real-world GERT case studies in the oil & gas industry are often confidential. The following represent hypothetical examples illustrating potential applications.)

• Offshore Platform Construction: A GERT model could simulate the construction of an offshore platform, considering weather delays, equipment failures, and logistical challenges. The simulation would provide insights into project duration, cost, and risk.

• Oil Sands Extraction: A GERT network could model the extraction process, incorporating uncertainties related to bitumen recovery rates, equipment downtime, and environmental factors. This would enable a probabilistic assessment of project feasibility and profitability.

• Pipeline Installation: A GERT model could be used to assess the risk of pipeline installation projects considering potential delays due to unforeseen geological conditions, regulatory approvals, or equipment malfunctions. The model can help determine optimal resource allocation and contingency planning.

These case studies highlight how GERT can improve project planning, risk management, and decision-making in complex oil & gas projects by providing a quantitative framework to understand and mitigate uncertainties.