Reduce the risks and costs associated with exploration and production with world-class advanced geophysical interpretation and analysis from our HampsonRussell 2024 software. This comprehensive suite of reservoir characterization tools integrates well logs, seismic data and geophysical processes into an easily navigated, intuitive package for fast results. Known for its ease of use, HampsonRussell makes sophisticated geophysical techniques accessible. Experience powerful speed improvements for your processing projects with multi-node processing, Get the flexibility to design and code any process with the Python ecosystem while taking advantage of the HampsonRussell project structure and data access, Quickly import and use shapefiles, Work with your AWS and Microsoft Azure cloud data.
GeoAI encompasses a novel methodology for seismic reservoir characterization with limited well control, speeding up reservoir property predictions with a rock physics driven machine learning technique. Rock Physics theory and statistical simulations generate synthetic data for various geological scenarios. A simplified machine learning approach employs Convolutional Neural Networks (CNN) estimating multiple rock property volumes in a greatly simplified workflow.
Benefits of GeoAI:
Improves Reservoir Characterization for low well-control areas
Allows direct prediction of facies and reservoir properties
Utilizes rock physics guided machine learning to optimize extraction of information and value addition from all available data.
In standard supervised machine learning approaches, the seismic-to-rock property relationship is learned using available data. These methods, particularly deep learning, depend on having enough labeled data to adequately train the neural network. WellGen overcomes this challenge by generating synthetic data, simulating many pseudo-wells based on existing well statistics and rock physics modeling.
WellGen addresses common machine learning challenges, including:
Scarcity of wells within the study area
The difficulty of tying well data with seismic
High variability in the well curves not depicting geological variations
Inability to link geological and geophysical observations
Reservoir complexity that cannot be resolved by inversion alone
AVO is a comprehensive HampsonRussell module for pre-stack data conditioning, attribute computation and analysis. This module has the tools for conditioning pre-stack seismic data to produce optimum attribute volumes, cross-plotting and interpretation functions for locating AVO anomalies, and AVO modeling tools for calibration. Customizable workflows and the integration of AVO tools into the Geoview interface make sophisticated AVO analysis simple.
Benefits of HampsonRussell AVO:
Provides a single comprehensive module for data conditioning, attribute calculation and analysis
High-grades pre-stack seismic data for inversions
Calibrates seismic data with model data
Enables simple navigation and comparison of multiple attribute volumes with seismic gathers
Strata performs both post-stack and pre-stack inversions. In the conventional post-stack domain, Strata analyzes post-stack seismic volumes to produce an acoustic impedance volume. In the pre-stack domain, Strata analyzes angle gathers or angle stacks to produce volumes of acoustic impedance, shear impedance and density.
Benefits of Strata:
Provides easy QC and inversion parameter optimization at well locations using inversion analysis
Enables access to multiple inversion techniques such as model-based, colored and sparse spike inversion algorithms
Produces elastic attribute volumes as outputs which are ideally suited to be used as inputs for Emerge attribute prediction, or LithoSI facies classification.
Emerge is a geostatistical, attribute prediction module that can predict property volumes using well logs and attributes from seismic data. The predicted properties can be any log types available, such as porosity, velocity, density, gamma-ray, lithology and water saturation. Emerge can also be used to predict missing logs or parts of logs by using existing logs that are common to the available wells with deep learning and deep-feed neural network (DFNN) techniques.
Benefits of Emerge:
Enables non-linear, high-resolution predictions using neural networks
Gives the ability to predict volumes of any data type
Allows all data to be used during training through internal cross-validation
Derives a measure of correlation and error at each well used in the training
Enhances prediction of missing log data by using multi-log and non-linear combinations
GeoSI is a geostatistical (single stack and multiple stack) inversion application that generates high-frequency stochastic models for high-resolution reservoir characterization and uncertainty analysis. It addresses the band-limited nature of deterministic inversion methods and integrates well data and seismic data at a fine scale within a stratigraphic geomodel framework.
Benefits of GeoSI:
Provides increased resolution integrating well and seismic data
Supports cascaded reservoir property simulation
Enables risk and uncertainty analysis using probability cubes
Facilitates interpretation and reservoir simulation by using a stratigraphic framework
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