Analyses of porosity and permeability variations captured by cross‐well seismic measurements and geophysical well logs at Port Mayaca aquifer, south Florida

2007 ◽  
Author(s):  
Jorge Parra ◽  
Chris Hackert ◽  
Emily Richardson ◽  
Ned Clayton
Keyword(s):  
South Florida ◽  
Well Logs ◽  
Porosity And Permeability ◽  
Geophysical Well Logs ◽  
Seismic Measurements

Porosity and permeability images based on crosswell seismic measurements integrated with FMI logs at the Port Mayaca aquifer, South Florida

2009 ◽  
Vol 28 (10) ◽  
pp. 1212-1219 ◽  
Author(s):  
Jorge Parra ◽  
Chris Hackert ◽  
Emily Richardson ◽  
Ned Clayton
Keyword(s):  
South Florida ◽  
Porosity And Permeability ◽  
Seismic Measurements

scholarly journals NMR AND WELL LOGS PETROPHYSICAL CHARACTERIZATION OF SANDSTONE FROM THE MARACANGALHA FORMATION, BAHIA, BRAZIL

2021 ◽  
pp. 239-252
Author(s):  
Nathália De Souza Penna ◽  
Joelson Da Conceição Batista ◽  
Suzan Sousa de Vasconcelos
Keyword(s):  
Gamma Ray ◽  
Production Capacity ◽  
Clay Content ◽  
Well Logs ◽  
Depth Interval ◽  
Porous Space ◽  
Petrophysical Parameters ◽  
Porosity And Permeability ◽  
Using Data ◽  
Geophysical Well Logs

The storage and production capacity of reservoir rocks can be estimated through some petrophysics characteristics involving the lithological identification of the constitute rocks, fluids nature in the porous space, porosity, permeability, saturation and clay content. The most popular tools for obtaining these petrophysical parameters are the conventional geophysical well logs. However, the determination of petrophysical parameters from tools based on the phenomenon of nuclear magnetic resonance (NMR) has gained prominence in recent decades. In this work, we analyzed rock samples from outcrops in Frades Island region, Bahia, Brazil, through laboratory NMR measurements, to estimate and evaluate the petrophysical properties of the Maracangalha Formation, one of the main hydrocarbons reservoirs in the Recôncavo Basin. The Sandstone samples were characterized in terms of porosity, permeability, saturation, and petrofacies. Finally, we calculated porosity, permeability, and clay content using data from gamma-ray, electrical and density logs, measured in a depth interval interpreted for Maracangalha Formation. These results corroborate with the obtained by NMR since, despite the effects of weathering and erosion on the samples used, the values of porosity and permeability obtained in NMR are in the range of values calculated from these profiles.

Machine Learning for Multiple Petrophysical Properties Regression Based on Core Images and Well Logs in a Heterogenous Reservoir

2021 ◽  
Author(s):  
Tao Lin ◽  
Mokhles Mezghani ◽  
Chicheng Xu ◽  
Weichang Li
Keyword(s):  
Machine Learning ◽  
Model Building ◽  
Image Features ◽  
Well Logs ◽  
Model Complexity ◽  
Rock Properties ◽  
Core Analysis ◽  
Petrophysical Properties ◽  
Blind Test ◽  
Porosity And Permeability

Abstract Reservoir characterization requires accurate prediction of multiple petrophysical properties such as bulk density (or acoustic impedance), porosity, and permeability. However, it remains a big challenge in heterogeneous reservoirs due to significant diagenetic impacts including dissolution, dolomitization, cementation, and fracturing. Most well logs lack the resolution to obtain rock properties in detail in a heterogenous formation. Therefore, it is pertinent to integrate core images into the prediction workflow. This study presents a new approach to solve the problem of obtaining the high-resolution multiple petrophysical properties, by combining machine learning (ML) algorithms and computer vision (CV) techniques. The methodology can be used to automate the process of core data analysis with a minimum number of plugs, thus reducing human effort and cost and improving accuracy. The workflow consists of conditioning and extracting features from core images, correlating well logs and core analysis with those features to build ML models, and applying the models on new cores for petrophysical properties predictions. The core images are preprocessed and analyzed using color models and texture recognition, to extract image characteristics and core textures. The image features are then aggregated into a profile in depth, resampled and aligned with well logs and core analysis. The ML regression models, including classification and regression trees (CART) and deep neural network (DNN), are trained and validated from the filtered training samples of relevant features and target petrophysical properties. The models are then tested on a blind test dataset to evaluate the prediction performance, to predict target petrophysical properties of grain density, porosity and permeability. The profile of histograms of each target property are computed to analyze the data distribution. The feature vectors are extracted from CV analysis of core images and gamma ray logs. The importance of each feature is generated by CART model to individual target, which may be used to reduce model complexity of future model building. The model performances are evaluated and compared on each target. We achieved reasonably good correlation and accuracy on the models, for example, porosity R2=49.7% and RMSE=2.4 p.u., and logarithmic permeability R2=57.8% and RMSE=0.53. The field case demonstrates that inclusion of core image attributes can improve petrophysical regression in heterogenous reservoirs. It can be extended to a multi-well setting to generate vertical distribution of petrophysical properties which can be integrated into reservoir modeling and characterization. Machine leaning algorithms can help automate the workflow and be flexible to be adjusted to take various inputs for prediction.

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