how to use these analysis - reu houston 2017 | reserves...

Copyright © Intelligent Solutions, Inc. 2014

How to Use These Analysis

• The results of this analyses is used in a multiple step process to optimize:• Well trajectory design,

• Completion/Stimulation design 1. Select a location

2. Generate reservoir characteristics for the location

3. Use default design (field, cluster or offsets)

4. Run the predictive model

5. Optimize interactively

6. Perform Uncertainty Analysis

54


Design Optimum Frac Job

55

1. Select a location



NOTE: All data presented here have been modified to protect confidentiality.



56




Porosity




57




PorositySwi




58




PorositySwi




59

5. Optimize interactively



60

6. Perform Uncertainty Analysis




61


Thank You for your Attention !

Questions?


Shale Formation EvaluationData-Driven Synthetic Geo-mechanical Logs

Marcellus Shale


• Introduction

• Artificial intelligence & Data Mining

• Methodology & Workflow

• Results & Discussions

• Conclusions

OUTLINE


• 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



• 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.



• 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


• 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“Synthetic Geo-Mechanical Well Logs for Shale” - Sajjad Omidvar: M.S. Thesis, West Virginia University, Petroleum & Natural Gas Engineering- Summer 2013


• 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“Synthetic Geo-Mechanical Well Logs for Shale” - Sajjad Omidvar: M.S. Thesis, West Virginia University, Petroleum & Natural Gas Engineering- Summer 2013


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

“Synthetic Geo-Mechanical Well Logs for Shale” - Sajjad Omidvar: M.S. Thesis, West Virginia University, Petroleum & Natural Gas Engineering- Summer 2013


Training & Blind Wells“Synthetic Geo-Mechanical Well Logs for Shale” - Sajjad Omidvar: M.S. Thesis, West Virginia University, Petroleum & Natural Gas Engineering- Summer 2013


Results (Blind Well #1)“Synthetic Geo-Mechanical Well Logs for Shale” - Sajjad Omidvar: M.S. Thesis, West Virginia University, Petroleum & Natural Gas Engineering- Summer 2013


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



Training & Blind Wells“Synthetic Geo-Mechanical Well Logs for Shale” - Sajjad Omidvar: M.S. Thesis, West Virginia University, Petroleum & Natural Gas Engineering- Summer 2013


Results (Blind Well #5)

?



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



30 Wells with Geomech. Logs- Well # 5 Logs



88

30 Wells with Geomech. Logs- Well # 5 Logs



89

80 Wells with Sonic porosity- Well # 5 Logs



• 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


Slide 91

Synthetic Sonic for Blind Well #5


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


Bulk ModulusUsing 30 Wells

Using 80 Wells


Shear ModulusUsing 30 Wells

Using 80 Wells


Young ModulusUsing 30 Wells

Using 80 Wells


Poisson’s RatioUsing 30 Wells

Using 80 Wells


Total Minimum Horizontal StressUsing 30 Wells

Using 80 Wells


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

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

• Combining synthetic and actual Geo-Mechanical well logs in a shale asset, can generate Geo-Mechanical properties distribution models with good resolution and accuracy.

• Having Geo-Mechanical properties distribution models can assist completion engineers to design more effective hydraulic fractures.

Conclusions

how to use these analysis - reu houston 2017 | reserves...

Documents