Determining In-Situ Stress Profiles From Logs . Shahab D. Mohaghegh - - PowerPoint PPT Presentation

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Determining In-Situ Stress Profiles From Logs . Shahab D. Mohaghegh - - PowerPoint PPT Presentation

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SPE 90070 Determining In-Situ Stress Profiles From Logs . Shahab D. Mohaghegh West Virginia University Andrei Popa , Chevron-Texaco Razi Gaskari , WVU Steve W olhart , Pinnacle Technologies Robert Siegfreid , GTI & Sam Am eri ,

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Determining In-Situ Stress Profiles From Logs.

Shahab D. Mohaghegh West Virginia University

Andrei Popa, Chevron-Texaco – Razi Gaskari, WVU – Steve W olhart, Pinnacle Technologies – Robert Siegfreid, GTI & Sam Am eri, WVU

SPE 90070 SPE Annual Technical Conference, Houston, TEXAS, September 2004

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OUTLINE

Introduction Methodology Results & Discussion Conclusions

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Introduction

All hydraulic fracturing simulators

require information regarding in-situ stress.

An important factor in designing

successful hydraulic fractures.

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Introduction

The contrast between different

formation’s in-situ stress is the controlling factor for the shape of the hydraulic fracture.

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Introduction

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Introduction

Lack of accurate in-situ stress values

during the design of a hydraulic fracture can result in as much as 50% error in the actual fracture length upon implementation.

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Introduction

Currently there are two methods of

in-situ stress determination:

Direct measurements (Expensive) Calculate from logs (inaccurate)

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Introduction

The inaccuracy of calculating in-situ

stress from logs has its roots in lack

  • f incorporating tectonic stress

components in the calculation

Not an easy and inexpensive way to

measure it

Not sure how to incorporate it, even if

available.

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Introduction

This paper introduces a new method

for incorporating tectonic stress when modeling in-situ stress from logs.

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OUTLINE

Introduction Methodology Results & Discussion Conclusions

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Methodology

It was identified that formation

lithology is an important input for the model.

This was also shown in previous

studies such as “ABC” method.

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Methodology

To deduce formation lithology from

logs a fuzzy system was developed.

The fuzzy system uses three logs and

27 rules in order to decide on the formation lithology.

Gamma Ray Deep Induction Spontaneous Potential

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Methodology

Fuzzy system rules for determining formation lithology.

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Methodology

In order to incorporate tectonic stress

component in our analysis two parameters were identified as surrogate:

Spectral Response Acceleration Seismic Zone Factor

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Methodology

Uniform Building Code – 1991Edition Seismic Zone Map of the United States of America

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Methodology

Spectral Response Acceleration

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Methodology

These parameters are borrowed from

structural engineering where tectonic activities play an important role in design.

They have been used as an indicator

  • f tectonic activities in our study.
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Methodology

Neural Networks were used to

develop an in-situ stress model.

Model inputs are:

Logs Formation Lithology Tectonic Stress Component

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Methodology

Inputs to the

Neural Network Model.

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OUTLINE

Introduction Methodology Results & Discussion Conclusions

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Results

Existing models usually perform

reasonably well when applied to the wells that were used for these model’s development.

The real test would be their

performance on wells that were not used during the model development.

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Results

Four existing models are tested:

Staged Field Experiment (SFE) # 1 Staged Field Experiment (SFE) # 2 Staged Field Experiment (SFE) # 3 Staged Field Experiment (SFE) # 4

These models were developed for GRI

in the past decades.

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Results

All models applied to SFE # 2 measurements.

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Results

All models applied to measurements from Ashland SFOT and AT Mast wells.

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Results

All models applied to measurements from Hogsback and Anderson Canyon wells.

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Results

Neural model applied to measurements from a well that was not used during its development.

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Results

Neural model applied to measurements from a well that was not used during its development.

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OUTLINE

Introduction Methodology Results & Discussion Conclusions

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Conclusions

A new model for determining in-situ

stress from logs has been developed and presented.

The new model incorporates tectonic

stress component for in-situ stress determination

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Conclusions

The new model is demonstrated to be

superior to the existing techniques.

Neural networks and fuzzy logic were

the main techniques used to develop the new model.