Risk Based Decision Making

Making Smart Decisions: Avoiding Intuition Traps

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Have you ever made a snap decision that seemed right at the moment but wasn't the best choice in the long run? This is a common human tendency, especially when dealing with risk.

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This guide introduces Risk-Based Decision Making (RBDM), a framework to analyze situations logically and identify the most valuable course of action. It emphasizes the importance of good data and avoiding impulsive choices, even if they seem well-intentioned.

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The Intuition Trap: A Baseball Example

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Consider this question: a bat and ball cost $1.10 together, and the bat costs $1 more than the ball. What's the ball's price?

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Many people instinctively guess $0.10. But is that the logical answer based on the given information?

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This example highlights how intuition can lead us astray. RBDM helps us overcome this by considering all possible outcomes and their associated risks.

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Making Informed Decisions: The RBDM Process

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RBDM focuses on the expected value of each decision, which considers both potential gains and losses. We analyze each option based on:

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Probability of Occurrence: How likely is each outcome? We use data analysis tools and our experience to estimate these probabilities.

Value of Each Outcome: How much will we gain or lose in each scenario?

Be aware that our own gut feelings can influence these probabilities, so a structured approach is crucial.

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The best way to perform RBDM is through graphical decision tree analysis, a visual tool that helps us compare costs and benefits of different choices. We'll explore this method with some examples in the following sections.

Reaming prior to connections

Stop Reaming Unnecessarily: Focusing on Real Problems

In oil drilling, a common practice is to ream the hole even when there are no signs of trouble. This stems from the fear of potential problems and the high cost of fixing them later. However, this approach can be wasteful and even counterproductive.

Why Reaming Isn't Always Helpful

Logically, reaming only makes sense when there's clear evidence of a problem in the hole, identified while picking up the drill string after reaching depth. Reaming beforehand might actually hide potential issues that could surface during connection, when the drill string is stationary and circulation is stopped. It also doesn't affect the wellbore above a certain point and shouldn't be used to compensate for poor cleaning or drill string design.

Making the Right Call: Data-Driven Decisions

Let's analyze the decision to stop unnecessary reaming. Here's some sample data based on past problems (NPT):

• Cost of 10 minutes of average reaming: $250
• Cost of fixing problems caused by skipping reaming (e.g., additional reaming, stuck pipe): $2,000,000
• Chance of problems occurring even after reaming: 1 in 50,000
• Chance of problems occurring without reaming, but with no prior signs of trouble: 1 in 25,000 (conservative estimate)

As you can see, the risk of problems remains even after reaming, and the cost of unnecessary reaming adds up. By focusing on identifying actual problems before connection, we can avoid wasting resources and potentially make things worse.

This scenario is represented in the following decision tree diagram:

The Expected Value (EV) for reaming is -$40 per connection compared to the EV for not reaming of $169.99. This shows that it is more economical to not ream based on risk analysis. If there are no hole problems observed then not reaming will save $210 per connection. In 50 Plus Operational Rigs Organization we ream approximately 50,000 connections per year where there are no hole problems. Making the decision to not precautionary ream would save approximately

$10,000,000 per year.

Pressure testing completion before setting packer:

Optimizing Completion Testing: Balancing Risk and Cost

When installing a completion with a packer, both the completion string and the packer need pressure testing. There are two main approaches:

1. Test Completion First (Traditional Method): This involves setting plugs, pressure testing the completion string, and then setting the packer. While this method allows for easier rerunning in case of a failed test, it requires additional rig time and incurs more cost.
2. Test Completion After Setting Packer (Cost-Effective Method): This approach involves setting the packer first, then testing both the packer and completion string together. While a failed test requires a more complex rerun, it saves time and cost upfront.

Making the Smart Choice: Data-Driven Decisions

Here's some sample data to consider:

• Cost of pressure testing the completion before setting the packer: $15,000
• Cost of a failed test before setting the packer (rerunning completion): $50,000
• Cost of a failed test after setting the packer (more complex rerun): $500,000
• Probability of a failed pressure test: 1 in 200 (0.5%)

As you can see, the chance of a failed test is relatively low. Additionally, the cost savings from testing after setting the packer significantly outweigh the potential cost of a more complex rerun. This approach prioritizes cost-effectiveness without neglecting safety, considering the low probability of failure.

?This scenario is represented in the following decision tree diagram:

The EV for pressure testing the completion after setting the packer is $12,675 higher than the EV for testing before. ?Over the cycle of 200 completions, there will probably be one failure. If the completion is pressure tested first, the cost of failure would be significantly higher, but overall the cost benefit would be $2,500,000.

?Answer to the bat and ball question:

?It isn’t 10 cents; that is what your instincts are telling you it should be.? If it was 10 cents, the bat and ball together would cost $1.20. Work it out logically!