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SPE 163690

Synthetic Geomechanical Logs for Marcellus Shale

Eshkalak, M. O., Mohaghegh, S. D., & Esmaili, S. West Virginia University

Petroleum Engineering and Analytical Research Lab (PEARL)

• Introduction

• Artificial intelligence & Data Mining

• Methodology & Workflow

• Results & Discussions

• Conclusions

OUTLINE

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• Rock Geomechanical properties defines Principal Stress Profiles of a reservoir.

• Principal Stress Profiles are the key to understanding Fracture Characteristics.

• Having access to Geomechanical data can assist engineers and geoscientists during modeling and hydraulic fracture treatment design.

Geo‐Mechanical Well Logs

• Geomechanical Properties from logs:

– Shear, Young and Bulk Modulus (Mpsi)

– Total Minimum Horizontal Stress (psi/ft)

– Poisson’s Ratio

Geo‐Mechanical Well Logs

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Geo‐Mechanical Well Logs

• Running Geomechanical well logs (in all wells in a Shale asset) is not common practice.

• This may be attributed to the cost associated with running such logs.

• Building a model of Geomechanical properties distribution (Map and Volume) for a shale can have many advantage:

– Assist in understanding the rock mechanical behavior, 

– Assist in designing effective hydraulic fractures.

Geo‐Mechanical Well Logs

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Artificial Intelligence & Data Mining

• Descriptive Data Mining

– Using the data in its current form and process it to deduce existing patterns.

• Predictive Data Mining

– Using the data in its current in order to develop predictive models to deduce existing patterns as well as patterns that may emerge.

Artificial Intelligence & Data Mining

• Artificial Intelligence provides the set of tools and techniques that makes modern data mining possible.

•

• Artificial Intelligence plays a key role in both descriptive and predictive data mining.

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Artificial Intelligence & Data Mining

• A collection of several analytical tools that attempts to imitate life.

• Exhibit an ability to learn and deal with new and dynamic situations.

• Possess attributes of reason such as generalization, discovery, association and abstraction.

Artificial Intelligence & Data Mining

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Artificial Intelligence & Data Mining

• The methodology used to accomplish the objectives of this study includes Four steps:

– Data Preparation

– Data Driven Model Development

– Model Validation

– Geomechanical Property Distribution (Map, Volume)

Methodology

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• Identifying the depth of the producing zones for Marcellus Shale.

• Extracting available data for each individual well for every foot.

Data Preparation

Data Base Information

Well Name Well Depth Well Coordinates

Gamma Ray (GR) Bulk Density (BD) Sonic Porosity

Bulk Modulus (BM) Shear Modulus (SM) Young’s Modulus (YM)

Poisson’s Ratio (PR) Total Minimum Horizontal Stress (TMHS)

• Every well has Gamma Ray Logs (80 wells)

• A subset of wells have Bulk Density and Sonic Logs (50 wells)

• Even a smaller subset of wells have GeoMech Logs (30 wells)

Data Preparation

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• The prepared database was processed using Neural Network:

– Conventional Models

– Generating synthetic Bulk Density and Sonic for the 30 wells with missing Sonic logs.

– Geo‐Mechanical Models

– Generating synthetic Geomechanical logs for 50 wells with missing Geomech logs.

Data‐Driven Model Development

Conventional ModelsModel (A) Development Procedure:

- Train the model using wells with Gamma Ray and Bulk Density Logs (Output: Bulk Density)

- Validate the model using Blind Wells (Output: Bulk Density)

- Generate Bulk Density for wells that are missing Bulk Density

• Location & Depth• Gamma Ray• General Facies Analysis

• Location & Depth• Gamma Ray• General Facies Analysis• Bulk Density

Bulk Density

Sonic Porosity

Model (B) Development Procedure:

- Train the model using wells with Gamma Ray, Bulk Density, & Sonic Logs (Output: Sonic)

- Validate the model using Blind Wells (Output: Sonic)

- Generate Sonic Logs for wells that are missing Sonic

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Training & Blind Wells

Results (Blind Well #1)

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Results (Blind Well #2)

Results (Blind Well #3)

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Results (Blind Well #4)

GeoMechanical Models

Model Development Procedure:

- Train the model using wells with Gamma Ray and Bulk Density & Sonic Logs

(Output: All Geomechnical Logs)

- Validate the model using Blind Wells (Output All Geomechnical Logs)

- Generate Bulk, Shear, and Young’s Modulus as well as Poisson’s Ratio and Minimum Horizontal Stress, for wells that are missing Geomechnical Properties.

• Location & Depth• General Facies

Analysis• Gamma Ray• Bulk Density• Sonic

Geomechanical Logs:‐ Bulk Modulus‐ Shear Modulus‐ Young’s Modulus‐ Poisson’s Ratio‐Min. Horiz. Stress

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Training & Blind Wells

Results (Blind Well #1)

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Results (Blind Well #2)

Results (Blind Well #3)

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Results (Blind Well #4)

Results (Blind Well #5)

?

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80 Wells with Bulk Density and Gamma Ray- Well # 5 LogsSlide 29

Slide 30

30 Wells with Geomech. Logs- Well # 5 Logs

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30 Wells with Geomech. Logs- Well # 5 Logs

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80 Wells with Sonic porosity- Well # 5 Logs

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• Generate Sonic Log for the Blind Well #5 using the Synthetic Well Log Generation Model (Model B).

• Use the new (synthetic) Sonic Log and generate Geomechnical Log, and compare it with the actual Geomechnical Logs. 

Blind Well #5

• Location & Depth• Gamma Ray• General Facies Analysis• Bulk Density

Sonic Porosity

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Synthetic Sonic for Blind Well #5

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Results (Blind Well #5)

• Comparing distribution of geomechanical properties throughout the asset:

– Using the geomechanical logs from 80 wells versus 30 wells.

– Sequential Gaussian Simulation (SGS)

Geo‐Mechanical Property Distribution

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Bulk ModulusUsing 30 Wells

Using 80 Wells

Shear ModulusUsing 30 Wells

Using 80 Wells

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Young ModulusUsing 30 Wells

Using 80 Wells

Poisson’s RatioUsing 30 Wells

Using 80 Wells

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Total Minimum Horizontal StressUsing 30 Wells

Using 80 Wells

• Artificial Intelligence & Data Mining can be used effectively to generate synthetic GeoMechanical well logs.

• Artificial Intelligence & Data Mining can be used as a tool to quality check the existing well logs.

• Performing Geo‐Mechanical well logs selectively in a shale asset, can help generating distribution models of the Geo‐Mechanical properties for the entire asset with reasonably good resolution.

Conclusions

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• Authors would like to thank the members of the Petroleum Engineering & Analytical Research Lab (PEARL) at West Virginia University for their assistance and support. 

• We also thank and acknowledge the following companies for providing software:

– Intelligent Solutions Inc. for providing IDEA (for Data‐Driven Modeling)

– Schlumberger for providing PETREL (Modeling property distribution)

Acknowlegement

Thank You

Questions

Slide 44Slide 44

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Geo-Mechanical Well Logs