Education Days Houston 2019

12 - 15 November
Houston, United States
Call for papers

 Date Title  Instructor(s) Duration
12-13 November 2019  Mitigating Bias, Blindness and Illusion in E&P Creties Jenkins, Texas USA
 2 Days
12-13 Novemberr 2019 Integrated Seismic Acquisition and Processing Jack Bouska, Calgary, Canada  2 Days
14-15 November 2019  Challenges and Solutions in Stochastic Reservoir Modelling - Geostatistics, Machine Learning, Uncertainty Prediction Vasily Demyanov, UK  2 Days 

Courses Descriptions

Mitigating Bias, Blindness and Illusion in E&P Decision Making

Creties Jenkins

Course description

Decisions in E&P ventures are affected by cognitive bias, perceptual blindness, and various forms of illusion which permeate our analyses, interpretations and decisions. This two-day course examines the influence of these cognitive pitfalls and presents techniques that can be used to mitigate their impact.  

"Bias" refers to errors in thinking whereby interpretations and judgments are drawn in an illogical fashion. "Blindness" is the condition where we fail to see an unexpected event in plain sight. "Illusion" refers to misleading beliefs based on a false impression of reality.
All three—Bias, Blindness, and Illusion--can lead to poor decisions regarding which work to undertake, what issues to focus on, and whether to continue investing time, effort, and money in a given project. 

The course begins by examining how these cognitive errors affect us. Several different errors are discussed, including: Perceptual Blindness; Illusions of Potential, Knowledge and Objectivity; and Anchoring, Availability, Confirmation, Framing, Information, Overconfidence and Motivational Biases. Exercises, videos, and examples help illustrate how these manifest themselves in our daily activities and affect our judgment, often without us realizing it. We then focus on the oil and gas industry where drilling portfolios, production forecasts, resource assessments, and other activities are regularly impacted. Techniques are presented that can be used to mitigate cognitive errors and examples are shown where these techniques have worked.

A key element of the course are the mitigation exercises which give participants a chance to apply what’s been learned to real-life situations. For example, what elements of the “anchoring bias” led to the belief that the exploration potential of a prospect offshore Brazil was much greater than it turned out to be? Or, what elements of the “confirmation bias” led to a decision regarding which analogous data should be used to predict the outcome of a new drilling project?

The second day includes a series of exploration and appraisal case studies resulting in both positive and negative outcomes. Participants are asked to identify cognitive errors contributing to the project results, and which of these had the greatest impact. This is followed by a 3-hour, real-world exercise using project data to give participants practice in addressing cognitive errors. The exercise requires participants to list all of their assumptions followed by a list of the contrary assumptions. This is followed by an assessment of the impacts if the contrary assumptions are true, and what key types of data / analyses will be required to determine which set of assumptions are correct. Finally, the participants identify cognitive errors leading to the actual project outcome.  

The course concludes by presenting a summary ‘toolkit’ with mitigation techniques that can immediately be applied to project work and decisions. This includes a laminated card listing the various types of bias, blindness and illusion on one side, and the six key steps to mitigate these cognitive errors on the flip side. This helps participants immediately apply the concepts to their daily work.

Participants' profile

This course is designed to have broad appeal to all levels and disciplines within an organization: junior to senior level geoscientists, junior to senior level engineers, analysts,  landmen, HSE, HR, etc. And mid-level to senior managers and executives.


Integrated Seismic Acquisition and Processing    

Mr Jack Bouska

Course Description

This course covers both modern and future practices in 3D seismic acquisition survey design and field operations. The seismic experiment is introduced as part of a larger integrated system, one composed of acquisition design, field operations, data processing, imaging and interpretation. This two-day course emphasizes how real-world aspects of interpretation, data processing, imaging and field operations can either constrain or liberate various survey design parameter choices.

The course material conveys the full breadth of knowledge and tools required to select and adjust survey design parameters for optimum imaging of the subsurface target, while honouring equipment limits and surface constraints. The syllabus develops a practical set of survey design skills, using a combination of both presentations and in-class exercises. This knowledge and skill base is also reinforced using specific examples of cutting edge seismic acquisition projects from around the globe. All case histories were selected to emphasize the value of long offset, wide azimuth and simultaneous source techniques for onshore and offshore ocean bottom seismic acquisition 3D designs, employing both large and small field crews.

 Participants' profile

The course is designed for:

  1. Seismic acquisition specialists who wish to learn more about designing cost-effective acquisition programmes, that are well matched to state-of-the-art processing and imaging techniques, along with strategies to exploit the future of high channel count crews in order to create ultra-high quality images;
  2. Seismic processing specialists who wish to learn about how acquisition geometry parameter choice directly affects the ability to attenuate noise, and image the subsurface, in the context of a modern processing scheme.
  3. Seismic interpreters with a desire to know more about both of the above.


Challenges and Solutions in Sctochastic Reservoir Modelling - Geostatistics, Machine Learning, Uncertainty Prediction 

 Dr Vasily Demyanov

Course description

Reservoir prediction modelling is subject to many uncertainties associated with the knowledge about the reservoir and the way they are incorporated into the model. Modern reservoir modelling workflows, which are commonly based on geostatistical algorithms, aim to support development decisions by providing adequate reservoir description and predict its performance. Uncertainty about reservoir description needs to be accounted for in modelling workflows to quantify the spread of reservoir predictions and its impact development decisions.

The course aims to build awareness of the impact the modelling choices on the reservoir predictions and their relation to the way uncertainty is incorporated into reservoir modelling workflows. The course addresses the problem of tying the workflow with the expected geological vision of a reservoir subject to uncertainty. This is associated with one of the common issues, when standard assumptions of a workflow are not consistent with the model geology or do not reflect possible variations due to existing uncertainty.

The course demonstrates the implementation of geostatistical concepts and algorithms in geomodelling workflows and the ways uncertainty is accounted for in reservoir description and predictions. The course includes an overview of the state-of-the art conventional techniques and some novel approaches, in particular machine learning for reservoir description.


Machine learning provides new opportunities in data integration and the model control to tackle the modelling challenges related to non-stationary multi-scale correlation structure and complex connectivity patterns in reservoirs. Novel machine learning techniques are good at capturing dependencies from data, when their parametric description is difficult; and controlling the impact of noisy and ad-hoc data.


For more information and tailored advice, please visit our Education portal. 

Challenges and Solutions in Stochastic Reservoir Modelling - Geostatistics, Machine Learning, Uncertainty Prediction
Challenges and Solutions in Stochastic Reservoir Modelling - Geostatistics, Machine Learning, Uncertainty Prediction
Challenges and Solutions in Stochastic Reservoir Modelling - Geostatistics, Machine Learning, Uncertainty Prediction
Challenges and Solutions in Stochastic Reservoir Modelling - Geostatistics, Machine Learning, Uncertainty Prediction
Seismic Fracture Characterization: Concepts and Practical Applications
Seismic Fracture Characterization: Concepts and Practical Applications