Development of 1D-Synthetic Geomechanical Well Logs for Applications Related to Reservoir Geomechanics in Buzurgan Oil Field

Knowledge of the distribution of the rock mechanical properties along the depth of the wells is an important task for many applications related to reservoir geomechanics. Such these applications are wellbore stability analysis, hydraulic fracturing, reservoir compaction and subsidence, sand production, and fault reactivation. A major challenge with determining the rock mechanical properties is that they are not directly measured at the wellbore. They can be only sampled at well location using rock testing. Furthermore, the core analysis provides discrete data measurements for specific depth as well as it is often available only for a few wells in a field of interest. This study presents a methodology to generate synthetic-geomechanical well logs for the production section of the Buzurgan oil field, located in the south of Iraq, using an artificial neural network. An issue with the area of study is that shear wave velocities and pore pressure measurements in some wells are missing or incomplete possibly for cost and time-saving purposes. The unavailability of these data can potentially create inaccuracies in reservoir characterization n and production management. To overcome these challenges, this study presents two developed models for estimating the shear wave velocity and pore pressure using ANN techniques. The input parameters are conventional well logs including compressional wave, bulk density, and gamma-ray. Also, this study presents a construction of 1-D mechanical earth model for the production section of Buzurgan oil field which can be used for optimizing the selected mud weights with less wellbore problems (less nonproductive time. The results showed that artificial neural network is a powerful tool in determining the shear wave velocity and formation pore pressure using conventional well logs. The constructed 1D MEM revealed a high matching between the predicted wellbore instabilities and the actual wellbore failures that were observed by the caliper log. The majority of borehole enlargements can be attributed to the formation shear failures due to an inadequate selection of mud weights while drilling. Hence, this study presents optimum mud weights (1.3 to 1.35 g/cc) that can be used to drill new wells in the Buzurgan oil field with less expected drilling problems.

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Mechanical Rock Properties Estimation for Carbonate Reservoir Using Laboratory Measurement: A Case Study from Jeribe, Khasib and Mishrif Formations in Fauqi Oil Field

Iraqi Geological Journal ◽

10.46717/igj.54.1e.8ms-2021-05-29 ◽

2021 ◽

Vol 54 (1E) ◽

pp. 88-102

Author(s):

Qahtan Abdul Aziz ◽

Hassan Abdul Hussein

Keyword(s):

Mechanical Properties ◽

Bulk Density ◽

Depositional Environments ◽

Carbonate Reservoir ◽

Oil Field ◽

Well Logs ◽

Rock Properties ◽

Petrophysical Properties ◽

Empirical Correlations ◽

Rock Mechanical Properties

Estimation of mechanical and physical rock properties is an essential issue in applications related to reservoir geomechanics. Carbonate rocks have complex depositional environments and digenetic processes which alter the rock mechanical properties to varying degrees even at a small distance. This study has been conducted on seventeen core plug samples that have been taken from different formations of carbonate reservoirs in the Fauqi oil field (Jeribe, Khasib, and Mishrif formations). While the rock mechanical and petrophysical properties have been measured in the laboratory including the unconfined compressive strength, Young's modulus, bulk density, porosity, compressional and shear -waves, well logs have been used to do a comparison between the lab results and well logs measurements. The results of this study revealed that petrophysical properties are consistent indexes to determine the rock mechanical properties with high performance capacity. Different empirical correlations have been developed in this study to determine the rock mechanical properties using the multiple regression analysis. These correlations are UCS-porosity, UCS-bulk density, UCS-Vs, UCs-Vp Es-Vs, Es-Vp, and Vs-Vp. (*). For example, the UCS-Vs correlation gives a good determination coefficient (R2= 0.77) for limestone and (R2=0.94) for dolomite. A comparison of the developed correlations with literature was also checked. This study presents a set of empirical correlations that can be used to determine and calibrate the rock mechanical properties when core samples are missing or incomplete.

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Bridging Precipitate Microstructure and Mechanical Hardness of Nickel-Based Superalloy Through Artificial Neural Network Model

Volume 12: Mechanics of Solids, Structures, and Fluids ◽

10.1115/imece2020-23515 ◽

2020 ◽

Author(s):

Yangping Li ◽

Yangyi Liu ◽

Sihua Luo ◽

Zi Wang ◽

Ke Wang ◽

...

Keyword(s):

Neural Network ◽

Mechanical Properties ◽

Artificial Neural Network ◽

Cooling Rate ◽

Volume Fraction ◽

Sem Images ◽

Mechanical Hardness ◽

Nickel Based Superalloy ◽

Artificial Neural ◽

Artificial Neural Network Ann

Abstract The attractive mechanical properties of nickel-based superalloys primarily arise from an assembly of γ′ precipitates with desirable size, volume fraction, morphology and spatial distribution. In addition, the solutioning cooling rate after super solvus heat treatment is critical for controlling the features of γ′ precipitates. However, the correlation between these multidimensional parameters and mechanical hardness has not been well established to date. Scanning electron microscope (SEM) images with different γ′ precipitates were investigated in this study, and artificial neural network (ANN) method was used to build a microstructure-mechanical property model. The critical step in this work is to extract different microstructural features from hundreds of SEM images. In order to improve the accuracy of prediction, the cooling rate was also considered as the input. In this work, the methodology was proved to be capable of bridging microstructural features and mechanical properties under the inspiration of material genome spirit.

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A Model on the Correlation between Composition and Mechanical Properties of Mg-Al-Zn Alloys by Using Artificial Neural Network

Materials Science Forum ◽

10.4028/www.scientific.net/msf.488-489.793 ◽

2005 ◽

Vol 488-489 ◽

pp. 793-796 ◽

Cited By ~ 1

Author(s):

Hai Ding Liu ◽

Ai Tao Tang ◽

Fu Sheng Pan ◽

Ru Lin Zuo ◽

Ling Yun Wang

Keyword(s):

Neural Network ◽

Mechanical Properties ◽

Artificial Neural Network ◽

Magnesium Alloys ◽

Tensile Yield Strength ◽

Data Set ◽

The Neural Network ◽

Artificial Neural ◽

Prediction Of Mechanical Properties ◽

Artificial Neural Network Ann

A model was developed for the analysis and prediction of correlation between composition and mechanical properties of Mg-Al-Zn (AZ) magnesium alloys by applying artificial neural network (ANN). The input parameters of the neural network (NN) are alloy composition. The outputs of the NN model are important mechanical properties, including ultimate tensile strength, tensile yield strength and elongation. The model is based on multilayer feedforward neural network. The NN was trained with comprehensive data set collected from domestic and foreign literature. A very good performance of the neural network was achieved. The model can be used for the simulation and prediction of mechanical properties of AZ system magnesium alloys as functions of composition.

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Prediction of Mechanical Properties of Stainless Steel Using an Artificial Neural Network Model

Reference Module in Materials Science and Materials Engineering ◽

10.1016/b978-0-12-803581-8.04148-5 ◽

2017 ◽

Cited By ~ 1

Author(s):

G.H. Senussi

Keyword(s):

Neural Network ◽

Mechanical Properties ◽

Artificial Neural Network ◽

Stainless Steel ◽

Network Model ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Artificial Neural ◽

Prediction Of Mechanical Properties

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New Artificial Neural Network Model for Predicting the TOC from Well Logs

10.2118/194716-ms ◽

2019 ◽

Cited By ~ 1

Author(s):

Abdullah Sultan

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Model ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Well Logs ◽

Artificial Neural

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Application of a convolutional neural network in permeability prediction: A case study in the Jacksonburg-Stringtown oil field, West Virginia, USA

Geophysics ◽

10.1190/geo2018-0588.1 ◽

2019 ◽

Vol 84 (6) ◽

pp. B363-B373 ◽

Cited By ~ 7

Author(s):

Zhi Zhong ◽

Timothy R. Carr ◽

Xinming Wu ◽

Guochang Wang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Regression Model ◽

Bulk Density ◽

Carbon Storage ◽

Oil Field ◽

Well Logs ◽

Storage Site ◽

Core Samples ◽

Permeability Prediction

Permeability is a critical parameter for understanding subsurface fluid flow behavior, managing reservoirs, enhancing hydrocarbon recovery, and sequestering carbon dioxide. In general, permeability is measured in the laboratory based on subsurface core samples, calculated from well logs or estimated from well tests. However, laboratory measurements and well tests are expensive, time-consuming, and usually limited to a few core samples or wells in a hydrocarbon field or carbon storage site. Machine-learning techniques are good options for generating a rapid, robust, and cost-effective permeability prediction model because of their strengths to recognize the potential interrelationships between input and output variables. Convolutional neural networks (CNN), as a good pattern recognition algorithm, are widely used in image processing, natural language processing, and speech recognition, but are rarely used with regression problems and even less often in reservoir characterization. We have developed a CNN regression model to estimate the permeability in the Jacksonburg-Stringtown oil field, West Virginia, which is a potential carbon storage site and enhanced oil recovery operations field. We also evaluate the concept of the geologic feature image, which is converted from geophysical well logs. Five variables, including two commonly available conventional well logs (the gamma rays [GRs] and bulk density) and three well-log-derived variables (the slopes of the GR and bulk density curves, and shale content), are used to generate a geologic feature image. The CNN treats the geologic feature image as the input and the permeability as the desired output. In addition, the permeability predicted using traditional backpropagation artificial neural networks, which are optimized by genetic algorithms and particle swarm optimization, is compared with the permeability estimated using our CNN. Our results indicate that the CNN regression model provides more accurate permeability predictions than the traditional neural network.

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