Education Days Houston 2019

21 - 22 November
Houston, United States
Call for papers
 Date Title  Instructor(s) Duration
November 2019  Mitigating Bias, Blindness and Illusion in E&P Creties Jenkins, Texas USA
 2 Days
 November 2019 Integrated Seismic Acquisition and Processing Jack Bouska, Calgary, Canada  2 Days
 November 2019  Challenges and Solutions in Stochastic Reservoir Modelling - Geostatistics, Machine Learning, Uncertainty Prediction Vasily Demyanov, UK  2 Days
 November 2019   Seismic Fracture Characterization: Concepts and Practical Applications  Enru Liu, Texas, USA  1 Day



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 modern techniques in 3D seismic acquisition, introducing the seismic experiment as an integrated system composed of acquisition      design, field operations, data processing, imaging and interpretation. This two-day course emphasizes how practical aspects of interpretation, data    processing, imaging and/or field operations can either constrain, or liberate various survey design parameter choices.

The concept of adjusting survey design parameters for optimum imaging of the subsurface target, while honouring equipment and surface constraints, will be reinforced by using in class exercises, with examples of cutting edge seismic acquisition projects from around the world. These case histories will emphasize wide aperture, wide azimuth and multi-azimuth techniques for onshore, offshore and OBC acquisition 3D designs.

Participants' profile

  1. Seismic acquisition specialists who wish to learn how to design cost-effective acquisition programmes that take advantage of modern state-of-the-art processing and imaging techniques;
  2. Seismic processing specialists who wish to learn some novel processing techniques to overcome perceived limitations in acquisition geometries;
  3. Seismic interpreters who wish 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.


Participant's Profile

The course is designed for a wide audience of reservoir modellers, geologists and engineers with range of experience from novices to experienced practitioners.


Seismic Fracture Characterization: Concepts and Practical Applications

Enru Liu

Course Description 

The ability to identify fracture clusters and corridors and their prevalent directions within many carbonates and unconventional resources (shale gas, tight gas and tight oil reservoirs) can have a significant impact on field development planning as well as on the placement of individual wells. The characterization of natural fractures is difficult and cannot be achieved by any single discipline or single measurement. We believe that geophysics is the only method that is able to identify spatial distributions of fractures and fracture corridors between wells, and seismically-derived fracture information complement (not compete with) other measurements, such as outcrops, core, FMI, cross-dipole and other fracture information. This book and the associated course provide an introduction to the fundamental concepts of seismic fracture characterization by introducing seismic anisotropy, equivalent-medium representation theories of fractured rock and methodologies for extracting fracture parameters from seismic data. We focus on practical applications using extensive field data examples. Three case studies are included to demonstrate the applicability, workflow and limitations of this technology: a physical laboratory 3D experiment where fracture distributions are known, a Middle East fractured carbonate reservoir and a fractured tight gas reservoir.


Participants' profile

The integrated nature of this subject means that the book and the associated course are purposely designed for individuals from all subsurface disciplines including geophysics, geomechanics, rock physics, petrophysics, geology, reservoir modelling and reservoir engineering.



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