Workshop 18
Improving Subsurface Decision Quality Friday, June 12th|
Convenors
- Jeff Parke (BP)
Description
- Learn methods to structure and validate subsurface predictions, reducing cognitive biases.
- Conduct risk assessments early to identify relevant reference class analogues, enabling more realistic forecasting and robust decisions.
- Use practical tools such as scenario planning, reference class forecasting, and probabilistic modelling to counter overconfidence and anchoring.
Sub-Topics that will be covered in the workshop:
Role of subsurface teams in decision-making processes.
Understanding Bias in Subsurface Predictions
- Common cognitive biases: overconfidence, anchoring, and confirmation bias.
- Impact of bias on volumetric estimates and risk profiles.
De-biasing Techniques
- Scenario planning and structured uncertainty ranges.
- Pre-mortem analysis to anticipate failure modes.
- Reference class forecasting: principles and application.
Risk Assessment and Analogue Identification
- Building risk matrices for exploration and development projects.
- Identifying and validating reference class analogues using historical data.
- Linking analogue insights to probabilistic forecasts.
Practical Exercises
- Case studies on bias reduction in volumetric estimation.
- Hands-on analogue benchmarking and scenario modelling.
- Group discussion: improving input quality for high-stakes decisions.
Participant Profile
Geoscientists, Reservoir Engineers, and Decision makers involved in exploration and development projects.
Experience Level: Mid-career professionals with 5–15 years of experience in subsurface evaluation, risk analysis, or project planning.
Responsibilities:
- Provide technical input for investment decisions.
- Assess uncertainty and risk in subsurface models.
- Communicate forecasts and recommendations to decision makers.
Motivation: Improve the reliability and clarity of predictions, reduce bias in evaluations, and strengthen decision quality under uncertainty.
Prerequisites: Familiarity with subsurface workflows, basic probabilistic concepts, and project economics.
Workshop Programme
Coming Soon!
| Time | Activity |
|---|---|
| 09:30 | Welcome Remarks |
| . | Session 1: Big picture perspectives |
| 09:35 | From early experiments to value generation today, a DAS journey: M. Thompson (Equinor) |
| 09:55 | Exploring DAS seismic for active and passive monitoring: highlights and challenges: A. Calvert (TotalEnergies), E. Rebel (TotalEnergies) |
| 10:15 | Exploring our DAS technology approval process: E. Raknes (Aker BP) |
| 10:35 | Experience of the world’s largest 3D DAS-VSP and the world’s first in a carbonate saline aquifer for CO2 plume monitoring: G. Cambois (ADNOC) |
| 10:55 | Coffee break |
| 11:10 | 11:10 Session 1 panel discussion |
| . | Session 2: How we acquire and handle data |
| 11:30 | Advancing Geothermal Monitoring with Distributed Acoustic Sensing: Insights from Utah FORGE and UKGEOS: A. Chalari (Luna) |
| 11:45 | DAS seismic data acquisitions – challenges and optimisations: H. Nakstad (ASN) |
| 12:00 | DAS in Mineral Exploration Challenges and Innovations in Ecologically Sensitive Environments; C. Cosma (Vibrometrics), V. Lanticq (Febus-Optics) |
| 12:15 | The implementation of a near-real-time DAS processing pipeline; B.Clapp (Google X) |
| 12:30 | Handling large data streams from energy using Microsoft cloud: F. Odinson (Microsoft) |
| 12:45 | Lunch break |
| 13:30 | Session 2 panel discussion |
| . | Session 3: How we use data and new possibilities |
| 13:50 | Lessons learned from the Otway Stage 4 experiment: R. Pevzner (Curtin University) |
| 14:05 | Advancing Sensitive Injection Monitoring: The Bureau’s Fiber-Enabled Field Laboratory at Devine and Telecommunication Fiber Sensing Across the Gulf Coast: A. Bakulin (BEG) |
| 14:20 | Practical uses of fiber optical sensing applications from the Centre for Geophysical Forecasting: M. Landro (NTNU) |
| 14:35 | Coffee break |
| 14:50 | Challenges and solutions associated with S-DAS data: R. Bachrach (SLB) |
| 15:05 | Highlighting the processing and imaging challenges of DAS data: K. Liao (Viridien) |
| 15:20 | Addressing the challenge of DAS data: I. Vasconcelos (Shearwater) |
| 15:35 | Session 3 panel discussion |
| 15:55 | Wrap-up discussion |
| 16:00 | End of workshop |