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Events

Technical Sessions - North America Series

April 09 - May 29, 2020
11:30 AM CDT (unless otherwise noted)
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Our current environment has been profoundly changed, forcing all of us to work from home and connect differently, while facing growing challenges in the oil and gas industry.

In light of these circumstances, Emerson is introducing a series of online Technical Sessions covering the breadth of geoscience domains; from seismic depth imaging to reservoir modeling. We hope you will use this time to sharpen skills, learn new methods to help you improve efficiency, and connect with a geoscience subject matter expert who will answer your questions during each live session. 

We invite you to register for any or all of the lectures. 

Date

Title

Register

Efficiency-driven Subsurface INTERPRETATION 

 
Thursday, April 9
11:30 AM CT

Presented by: 
Elive Menyoli
Model-based Tomography for Accessing Subsurface Structural Uncertainty  
With this tomographic approach, you will learn to assess and quantify the impact of velocity model errors on subsurface structures (fault zones and horizon position errors). Key results of the workflow are newly positioned target horizons/faults from the perturbed velocity models and kinematically equivalent velocity models that honor geophysical assumptions of seismic gather flatness. For each horizon and fault, the output contains a set of possible (x, y, depth) locations of the structure that may be directly fed into Emerson SKUA-GOCAD to build multiple geologic models needed for subsequent analyses. 
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Tuesday, April 21
11:30 AM CT

Presented by:
Dennis Ellison
Workflow for Field Development: Reducing Costs and Improving Decisions Using Machine Learning to Build Facies Models from Well and Seismic Data 
Integrating well and seismic data using traditional methods like seismic inversion is both time consuming and budget-intensive, while time and budget are two resources that are becoming tighter.  We at Emerson E&P Software have a long tradition of integrating ML/AI-based tools in our workflows to accelerate and produce more robust data products. In this session you will see how ML/AI can help you improve digital representations of the subsurface, such as rock prediction volumes, to better leverage all existing data, QC and de-risk them, and represent them in a comprehensive manner to ultimately make faster, more educated business decisions at lower cost. 
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Friday, May 15
11:30 AM CT

Presented by:
Elive Menyoli
 
An Efficient Workflow for Stochastic Inversion
This session will show you how Emerson's stochastic inversion helps our customers de-risk reservoir models and identify new drilling targets in their acreage. Our unique approach is based on the idea that in the stochastic inversion, results, well data and seismic data contribute to different frequency bands. Classic stochastic inversion processes involve huge numbers of stochastic predictions, from which the ones with the best match to the seismic data are selected. In this workflow, the deterministic inversion output is combined with numerous broadband-frequency well simulations to generate multiple broadband realizations of the reservoir, all of which match the seismic. Incorporating the deterministic inversion results into the modified stochastic inversion process simplifies it, thus significantly reducing costs while increasing reservoir properties knowledge. 
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Precision-Focused FORMATION EVALUATION

 
Thursday, April 9
2:15 PM CT

Presented by:
Kim McLean
Integrated Petrophysical Interpretation to Unlock Unconventional Reservoirs  
Shale reservoirs are heterogeneous in nature, with facies that differ in mineralogy and geomechanical properties. In this workflow you’ll learn how to evaluate those rock properties for an offset well, and how to incorporate that information into the planning stages of a horizontal well. Ideally, geologists could take advantage of their knowledge of mineralogical and geomechanical facies to optimally place a well that leads to better placement within the reservoir sweet spot. 
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Friday, April 24
11:30 AM CT

Presented by:
Kim McLean
Reduce Drilling Risks and Costs using Pore Pressure Prediction and Geomechanics  
In order to ensure well stability and assess optimal orientation for fracture development, it is important to understand regional stress orientations and magnitudes, and how these may impact rock properties in the formations around a wellbore. The Pore Pressure Prediction and Geomechanics workflow in Geolog provides users with a comprehensive workflow to help assess regional stress regimes and the mechanical conditions around the wellbore, and ultimately aid in reducing drilling risks while maximizing well and reservoir productivity. 

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Thursday, May 7
11:30 AM CT

Presented by:
Kim McLean
Facimage: A Machine Learning-based Approach (co-developed with Total SA) for Faster and More Precise Rock Type Classification  
Well log data provides a wealth of information about the subsurface, from mineralogy to porosity, permeability and saturation. We will look at ways to leverage our wireline and petrophysical data using an advanced electrofacies toolkit complete with clustering and neural network routines for electrofacies analysis, ultimately leading to a better understanding of our reservoir rock properties. We’ll look at facies identification in non-cored intervals to predict petrophysical properties using Multi-Resolution Graph-based Clustering (MRGC). 
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Friday, May 22
11:30 AM CT

Presented by:
Kim McLean

Scripting in Geolog
Today, data analytics and predictive models are essential components of any geoscience workflow, because of their added-productivity and automation capacities. The Geolog petrophysical solution provides the ability to run built-in robust algorithms while also giving users the power to strengthen their analytics and modeling capabilities by creating custom scripts. 

In this presentation, you will learn more about:

  • How to customize your workflows and build your own code snippets, using the well-established LOGLAN logging language, Matlab or Python 
  • How to leverage Python third-party libraries within Geolog
  • How to build custom queries to start looking at data analytics in Geolog using the Geolog SQL query tool
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Managing Uncertainties and Fast-Updating workflows for RESERVOIR MODELING  

 
RMS Series 1    
Tuesday, April 14
11:30 AM CT

Presented by:
Sasan Ghanbari
Well Planning and Geosteering with RMS: Time-saving and Cost-effective 
One of the key objectives in building a reservoir model is to be able to identify potential well targets and plan the wells and well trajectory. Using Emerson RMS, multidisciplinary teams can design targets quickly and efficiently, and optimize well paths to reduce drilling costs.  The geosteering module helps to avoid any problems associated with the geological structure of the reservoir object during drilling. See how RMS enables asset teams to visualize 3D subsurface models together with the new well planned trajectory, and empowers the user with a better decision-making process to successfully geosteer the well within the pay area.   
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Monday, April 20
11:30 AM CT

Presented by:
Andrey Klimushin
Roxar API : Python Scripting for Geomodelers 
Learn how to extend the functionality of the RMS geomodeling platform using libraries of pre-existing, customizable Python scripts, or bring your own scripts (open source or not) and plug them in. The possibilities are literally endless! During this session, you will see how Roxar’s API (Application Programming Interface) helps to bridge the data science and geoscience worlds, through easy steps with Python-based scripts.  It is simple and straightforward, and was designed for geoscientists, both those with prior experience with Python coding and those without.  Prerequisites: An analytic mind and curiosity!   
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Wednesday, May 6
11:30 AM CT

Presented by:
Sasan Ghanbari
Saving Costs in Heavy Oil and Unconventional Fields; Facies Modeling and SAGD Volume Calculation Using RMS  
Understand how facies modeling and SAGD volume calculations help major and independent oil operators reduce costs by optimizing pads and well placements, and ensure the best production results. This involves incorporating all data types and identifying geological trends that will impact the rock property distribution in the model.  You will learn how RMS helps to identify sufficient quality and thickness for the SAGD process to be both effective and economical, with an understanding of risk and uncertainty.  
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SKUA-GOCAD Series  
Friday, April 17
11:30 AM CT

Presented by:
Sasan Ghanbari
Getting More from Your Data: Exploratory Data Analysis with SKUA  
A reservoir property model is only as good as the parameters and data used to generate it. See how SKUA can extract, QC, validate, organize, transform, and analyze subsurface data, setting the base for a robust reservoir properties workflow. The data and trend analysis workflow in SKUA helps ensure that your models are robust and representative. Think of this as quality data in/quality model out! 
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Monday, April 27
11:30 AM CT

Presented by:
Andrey Klimushin
Build Faster and More Precise Structural and Stratigraphic Models, Regardless of the Complexity of Your Geology 
See how the SKUA-GOCAD structural/stratigraphical modeling workflow has for decades helped majors, super majors and independents to model the most complex environments in an elegant and easy way. With a simple-to-use, workflow-based interface, you will learn how to obtain trustable results efficiently, even when the modeling gets tough, and how to handle complexities where all other tools fail.
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Thursday, April 30
11:30 AM CT

Presented by:
Sasan Ghanbari
Unleash the Potential of Your Microseismic Data with SKUA 
Microseismic is a costly data source that is typically not being leveraged as much as it could. In this session, you will see how SKUA-GOCAD can help you deep dive into your microseismic data, QC, validate and analyze them, and create compelling visualization products and data tables that can be used as input to feed predictive models and correlate fracture parameters with historical production data. 
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RMS Series 2    
Wednesday, May 13
11:30 AM CT

Presented by:
Andrey Klimushin
Rapid and Simple but Not Simplistic: Geological Interpretation Using RMS  
Combine the best of two worlds - seismic interpretation and geological modeling – within our intuitive and user-friendly RMS platform – and complete all the steps needed to build your structural model, from well correlation and bulk mapping, to conceptual modeling, efficiently and quickly. The alpha and omega of geology for modeling. 
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Thursday, May 21
11:30 AM CT

Presented by:
Sasan Ghanbari
De-risk Your Reservoir Architecture: Model-driven Interpretation and Structural Uncertainties 
The oil and gas industry faces two major challenges regarding geomodellng workflows. The first is geologic risk assessment – the industry requires enhanced solutions for quantifying geologic risk as it moves into more complex tectonic settings and more (economically) marginal prospects. The second challenge is productivity – the industry requires solutions that allow domain experts to quickly integrate and share knowledge across the prospect lifecycle. See how RMS can provide answers to these challenges with modern workflows, such as model-driven interpretation, to capture and quantify structural uncertainty interpretation and integrate these measurements into modeling workflows. 
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Wednesday, May 27
11:30 AM CT

Presented by:
Sasan Ghanbari
Fast Pass from Seismic to Simulation 
In today's volatile market, companies have to make the right decision faster than ever, especially in subsurface domains, which historically require longer time frames and greater accuracy. The Golden Mean between time and precision can be achieved using the unique RMS modeling platform. In this session you will see how quickly your data can be converted from seismic and geological studies to 3D static and dynamic models, which decisions you can make based on that, and how you can use the models afterwards
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Friday, May 29
11:30 AM CT

Presented by:
Andrey Klimushin
Reduce Unnecessary Spending on Equipment by Optimizing Geosteering and Well Planning
One of the key objectives when building a reservoir model is to be able to identify potential well targets and plan the wells and well trajectories. See how RMS enables multidisciplinary teams to quickly and efficiently design targets and optimize well paths to reduce drilling costs. The RMS geosteering module helps prevent problems associated with the geological structure of the reservoir being drilled. RMS enables asset teams to view the 3D model together with the new well, link it with a dynamic model, and choose the right top well equipment in advance, after the simulation loop. 
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Next-Gen Reservoir Engineering 

 
Tuesday, May 5
11:30 AM CT

Presented by:
Usman Aslam
Efficient History Matching Using an Ensemble-based Machine Learning Approach  
See how Tempest ENABLE, Emerson’s machine-learning based technology for assisted history matching, can help ease the time-consuming history matching process and help identify multiple calibrated models critical for reliable production forecasts. A step-by-step assisted history matching and ensemble-based prediction workflow will be explained in detail. 
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Tuesday, May 12
11:30 AM CT

Presented by:
Usman Aslam
An Innovative Approach to Quantifying PVT Uncertainty Using a State-of-the-art Proxy Model 
Learn how this technique performs dramatically better than traditional approaches and better quantifies PVT uncertainties. This session will feature the application of a proxy modeling approach to regressing an EOS while simultaneously quantifying uncertainty in the fluid characterization. The proposed technique was applied successfully to a PVT model based on a black-oil fluid sample obtained from an oil field in the Gulf of Mexico.    
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Tuesday, May 19
11:30 AM CT

Presented by:
Usman Aslam
A Collaborative Approach to Calibrating Subsurface Models for Reliable Production Forecasting and Field Development Planning 
See how this innovative ensemble-based technology – Big Loop - helped a super-major improve its E&P reservoir modeling efficiency by 60% while reducing overall costs by 20%. The technology enables asset teams to integrate static and dynamic reservoir modeling, and to capture uncertainties at every stage of the modeling workflow, allowing them to understand the impact of these uncertainties on the final decision-making process. Big Loop employs state-of-the-art machine learning algorithms to produce geologically consistent ensembles of history matched models calibrated to production and optionally 4D seismic data. These ensembles can be reliably used to forecast production performance from oil and gas fields. Repeatable and updatable, the Big Loop workflow enables the incorporation of data acquired after the model is built and history matched. 
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Biographies

Elive_Menyoli_sm.jpgDr. Elive Menyoli has over 15 years of experience in the oil and gas industry. Prior to joining Emerson, he worked at Marathon Oil and Total E&P USA, in deep water projects. Dr. Menyoli holds a MSc degree in Physics from the University of Goettingen, Germany and a PhD in Geophysics from the University of Hamburg, Germany.  He has authored numerous publications in seismic imaging and interpretation, with a recent emphasis on shale resource plays.

Dennis-Ellison_sm-(1).jpgIn his role as Technical Advisor – Geophysics, Dennis Ellison helps producers reduce costs and leverage their data with next generation technology. His career started in depth imaging of geologically complex land data and transitioned into Reservoir Characterization and Quantitative Interpretation, focusing on unconventional reservoir property prediction. Dennis holds a Master’s degree in Geology and Geophysics from the University of Calgary in Canada. He is a member of CSEG, SEG, SPE, EAGE, and APEGA.


Kim-McLean1_sm_crop.jpgKim McLean received her Bachelors of Science degree from the University of New Orleans before continuing to Central Washington University, where she studied the structural geology of the Tien Shan in Kyrgystan for her Master’s thesis. After receiving her MSc, Kim entered the energy industry, and now has over 15 years of experience. She worked at Halliburton and Paradigm before spending two years as the petrophysicist for the Pike Asset team with BP in Calgary.  At Emerson E&P Software, Kim applies her practical petrophysical experience to the work she does with the Geolog Formation Evaluation application.

Andrey_Klimushin.jpgAndrey Klimushin has over fourteen years of experience in subsurface modeling, business development and project execution.  He spent eight years working in Moscow in geological modeling and subsurface studies for national (CGE, Gazprom-neft) and international companies (AGR petroleum, Roxar). He joined Emerson in 2008 and has since taken on projects with increasing levels of responsibility. He spent several years as a project team leader and consultant for Emerson Mexico.  There he helped the Mexican national oil and gas company PEMEX to increase production in one of the biggest offshore fields in the world.  Andrey has experience in leading geomodeling applications, unique project delivery, and cross-disciplinary upstream technologies, including the digital oil field approach.  He has also been involved in business and marketing development on the software side. Andrey is a native of Moscow, Russia. He graduated from the Gubkin Russian State University of Oil and Gas with a Master’s Degree in Reservoir and Petroleum Geology.

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Sasan Ghanbari is currently principal geoscientist at Emerson. Sasan has extensive experience in offshore exploration and in the Canadian Oil Sands. He has also worked on various field studies and geostastical subsurface modeling in North America, South America, and the Middle East.  This work includes analyzing relevant data such as regional studies and uncertainty analysis to build realistic geological, geostatistical and reservoir simulation models. Sasan has been working with Emerson since 2004. He earned a degree in Petroleum Engineering from the University of Alberta and is a Professional Engineer.

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Usman Aslam is a Reservoir Engineer Advisor with Emerson’s Exploration and Production Software and worked with the company in Norway, the UK, and in the US. Usman has 14+ years of diversified and cross-disciplinary international consulting experience and is highly skilled in classic reservoir engineering, numerical simulation modeling, history matching, production forecasting, reserves and resource estimation and planning, production optimization, field development planning, and integrated reservoir studies. He has successfully delivered numerous complex onshore and offshore conventional and unconventional field development projects for the leading Oil and Gas E&P companies.