Porosity and Permeability Prediction in Low-Permeability Gas Reservoirs From Well Logs Using Neura Networks

1998 ◽  
Author(s):  
Terrilyn M. Olson
Keyword(s):  
Well Logs ◽  
Low Permeability ◽  
Gas Reservoirs ◽  
Permeability Prediction ◽  
Porosity And Permeability

Study on Production Performance Simulating the Low Permeability Dual Media Gas Reservoirs Pre and Post Fracturing

2011 ◽  
Vol 287-290 ◽  
pp. 86-91
Author(s):  
Li Ying Wang ◽  
Shu Sheng Gao ◽  
Wei Xiong ◽  
Hua Xun Liu
Keyword(s):  
Early Stage ◽  
Cross Flow ◽  
Flow Coefficient ◽  
Production Performance ◽  
Natural Fracture ◽  
Low Permeability ◽  
Gas Reservoirs ◽  
Penetration Ratio ◽  
Order Of Magnitude ◽  
Porosity And Permeability

Mathematical model of dual media reservoir fracturing wells was established and the corresponding numerical calculation program was developed based on the special relationship between porosity and permeability of dual media low permeability gas reservoirs. Through comparative analysis of numerical results of production performance pre and post fracturing, effects of cross flow coefficient and fracture penetration ratio were well studied. The results show that: after a period of production, pressure decline of the gas well decreases linearly with time, whether fracturing or not, showing pseudo-steady-state characteristics; in the early stage, pressure drop in the vertical well pre-fracturing is an order of magnitude larger than the post-fracturing well in the logarithmic coordinate; the less developed the natural fracture is, the smaller the cross flow coefficient is, and the more significant role the fracturing plays in yield increasing; when the fracture penetration ratio is between 0.25~0.50, it has less impact on production, so it is suggested that the fracture penetration ratio is controlled at about 0.25 in actual dual media dense gas reservoirs.

Committee neural networks for porosity and permeability prediction from well logs

2002 ◽  
Vol 50 (6) ◽  
pp. 645-660 ◽  
Author(s):  
Alpana Bhatt ◽  
Hans B. Helle
Keyword(s):  
Neural Networks ◽  
Well Logs ◽  
Permeability Prediction ◽  
Porosity And Permeability

The Seepage Pressure Distribution and the Capacity of Low-Permeability Reservoirs Gas

2013 ◽  
Vol 734-737 ◽  
pp. 1317-1323
Author(s):  
Liang Dong Yan ◽  
Zhi Juan Gao
Keyword(s):  
Mathematical Model ◽  
Pressure Distribution ◽  
Low Permeability ◽  
Flow State ◽  
Klinkenberg Effect ◽  
Gas Reservoirs ◽  
Gas Well ◽  
Seepage Pressure ◽  
Slippage Effect ◽  
Low Permeability Reservoirs

Low-permeability gas reservoirs are influenced by slippage effect (Klinkenberg effect) , which leads to the different of gas in low-permeability and conventional reservoirs. According to the mechanism and mathematical model of slippage effect, the pressure distribution and flow state of flow in low-permeability gas reservoirs, and the capacity of low-permeability gas well are simulated by using the actual production datum.

Gas Slippage Effect in Low Permeability Water-bearing Gas Reservoirs

2011 ◽  
Author(s):  
Xiaojuan Liu ◽  
Jian Yan ◽  
Yi Liu
Keyword(s):  
Low Permeability ◽  
Gas Reservoirs ◽  
Gas Slippage ◽  
Slippage Effect

Horizon Attributes and Fracture-Swarm Sweet Spots in Low-Permeability Gas Reservoirs

2000 ◽  
Author(s):  
B.S. Hart ◽  
R.A. Pearson ◽  
J.M. Herrin ◽  
T. Engler ◽  
R.L. Robinson
Keyword(s):  
Low Permeability ◽  
Gas Reservoirs ◽  
Sweet Spots

Influence of porosity and permeability heterogeneity on liquid invasion in tight gas reservoirs

2017 ◽  
Vol 37 ◽  
pp. 169-177 ◽  
Author(s):  
Xu Yang ◽  
Yingfeng Meng ◽  
Xiangchao Shi ◽  
Gao Li
Keyword(s):  
Tight Gas ◽  
Gas Reservoirs ◽  
Tight Gas Reservoirs ◽  
Permeability Heterogeneity ◽  
Porosity And Permeability

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.

scholarly journals Experimental investigation of porosity and permeability change caused by salting out in tight sandstone gas reservoirs

2018 ◽  
Vol 3 (6) ◽  
pp. 347-352 ◽  
Author(s):  
Lijun You ◽  
Zhe Wang ◽  
Yili Kang ◽  
Yushan Zhao ◽  
Dujie Zhang
Keyword(s):  
Experimental Investigation ◽  
Permeability Change ◽  
Gas Reservoirs ◽  
Tight Sandstone ◽  
Salting Out ◽  
Porosity And Permeability
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