📢 قابل توجه اعضای محترم کانال تخصصی ژئومکانیک نفت
🔹 عنوان: آموزش نحوه نمایش لاگ در نرم افزار Geolog 7
🔹 مدرس: مهندس مهدی باجولوند
◾️ این آموزش در سه قسمت تهیه شده است. برای مشاهده و دانلود ویدیوها روی لینک مربوطه کلیک نمایید (فیلتر شکن خود را خاموش کنید).👇📎📥

▫️◾️▫️◾️▫️◾️▫️◾️▫️◾️▫️◾️▫️◾️

▶️ Part1: https://www.aparat.com/v/OJ3kc
▶️ Part2: https://www.aparat.com/v/7nkUb
▶️ Part3: https://www.aparat.com/v/vzGiL

دوره مدلسازی ژئومکانیکی یک بعدی چاه با نرم افزار ژئولاگ در صورت تکمیل ظرفیت از هفته آینده شروع خواهد شد.
ظرفیت باقی مانده دو نفر.
فقط تا فردا فرصت باقی است.

وبینار:
*مدل سازی ژئومکانیکی با استفاده از نشانگرهای لرزه ای*

سخنران: جواد شریفی
کارشناس شرکت نفت مناطق مرکزی
دکتری زمین شناسی مهندسی

⏰ زمان برگزاری:

سه شنبه 1400/12/24- ساعت 20-18

شرکت در وبینار آزاد است.

لینک وبینار:
https://meetbk.kntu.ac.ir/b/h2k-mbc-9mp
رمز ورود: ۲۵۲۵۱۶

Prediction of permeability from well logs using a new hybrid machine learning algorithm

Abstract:
Permeability is a measure of fluid transmissibility in the rock and is a crucial concept in the evaluation of formations and the production of hydrocarbon from the reservoirs. Various techniques such as intelligent methods have been introduced to estimate the permeability from other petrophysical features. The efficiency and convergence issues associated with artificial neural networks have motivated researchers to use hybrid techniques for the optimization of the networks, where the artificial neural network is combined with heuristic algorithms. This research combines Social Ski-Driver (SSD) algorithm with the multilayer perception (MLP) neural network and presents a new hybrid algorithm to predict the value of rock permeability. The performance of this novel technique is compared with two previously used hybrid methods (Genetic Algorithm-MLP and Particle Swarm Optimization-MLP) to examine the effectiveness of these hybrid methods in predicting the permeability of the rock. The results indicate that the hybrid models can predict rock permeability with excellent accuracy. MLP-SSD method yields the highest coefficient of determination (0.9928) among all other methods in predicting the permeability values of the test data set, followed by MLP-PSO and MLP-GA, respectively. However, the MLP-GA converged faster than the other two methods and is computationally less expensive.

Published in the Journal of Petroleum
https://www.sciencedirect.com/science/article/pii/S2405656122000219?via%3Dihub

Why do research papers get rejected?

دوره مدلسازی ژئومکانیکی یک بعدی چاه با نرم افزار ژئولاگ
برای اطلاعات بیشتر لطفا با مدیریت کانال تماس بگیرید.

چهارمین کنفرانس ملی ژئومکانیک نفت
http://pgc2022.kntu.ac.ir/

به اطلاع پژوهشگران ارجمند می‌رساند زمان برگزاری چهارمین کنفرانس ملی ژئومکانیک نفت - نوآوری و فناوری به 11 الی 13 بهمن‌ماه 1401 انتقال یافته و لذا علاقمندان می‌توانند چکیده مقالات خود را تا 15 مهرماه 1401 از طریق سامانه همایش ارسال فرمایند.

دبیرخانه چهارمین کنفرانس ژئومکانیک نفت

🔰همايش بين المللى هوش مصنوعى ، علم داده و تحول ديجيتال در صنعت نفت و گاز

🗣 سخنرانان: مدعوین داخلی و خارجی

🗓 تاریخ برگزاری: ٢ و ٣ اسفند ۱۴۰۱

📍مکان برگزاری: تهران، میدان رسالت، خیابان هنگام، دانشگاه علم و صنعت ایران

⬅️ علاقمندان جهت کسب اطلاعات بیشتر از این همایش می‌توانند به لینک زیر مراجعه نمایند:
http://ai-oilandgas.iust.ac.ir

🆔 www.tg-me.com/AI2022OilGasConference

وبینار و کارگاه آموزشی (رایگان)
کاربرد روش همبستگی تصاویر دیجیتال در مهندسی شکست سنگ
The Application of Digital Image Correlation (DIC) in Rock Fructure Engineering
زمان ۱۱ اسفند۱۴۰۱ - پنجشنبه
ساعت ۱۰ الی۱۳

مدرس: دکتر منصور شرفی‌صفا
دانش‌آموخته مهندسی عمران (مکانیک سنگ) دانشگاه سیدنی

لینک جلسه:
Adobe: https://ac.aminidc.com/intlikiu
Google meet: https://meet.google.com/kea-ntmj-raq

برگزارکننده:
دانشگاه بین‌المللی امام خمینی (ره)
دانشکده فنی‌ومهندسی
گروه پژوهشی رویکرد هوشمند و پایدار در صنایع معدنی

ایمیل مکاتبات:
[email protected]

@PetGeoResearch

Machine-learning predictions of solubility and residual trapping indexes of carbon dioxide from global geological storage sites

Abstract
Ongoing anthropogenic carbon dioxide (CO2) emissions to the atmosphere cause severe air pollution that leads to complex changes in the climate, which pose threats to human life and ecosystems more generally. Geological CO2 storage (GCS) offers a promising solution to overcome this critical environmental issue by removing some of the CO2 emissions. The performance of GCS projects depends directly on the solubility and residual trapping efficiency of CO2 in a saline aquifer. This study models the solubility trapping index (STI) and residual trapping index (RTI) of CO2 in saline aquifers by applying four robust machine learning (ML) and deep learning (DL) algorithms. Extreme learning machine (ELM), least square support vector machine (LSSVM), general regression neural network (GRNN), and convolutional neural network (CNN) are applied to 6811 compiled simulation records from published studies to provide accurate STI and RTI predictions. Employing different statistical error metrics coupled with supplementary evaluations, involving score and robustness analyses, the prediction accuracy of the models proposed is comparatively assessed. The findings of the study revealed that the LSSVM model delivers the lowest RMSE values: 0.0043 (STI) and 0.0105 (RTI) with few outlying predictions. Presenting the highest STI and RTI prediction scores the LSSVM is distinguished as the most credible model among all the four models studied. The models consider eight input variables, of which the time elapsed and injection rate displays the strongest correlations with STI and RTI, respectively. The results suggest that the proposed LSSVM model is best suited for monitoring CO2 sequestration efficiency from the data variables considered. Applying such models avoids time-consuming complex simulations and offers the potential to generate fast and reliable assessments of GCS project feasibility. Accurate modeling of CO2 storage trapping indexes guarantees successful geological CO2 storage operation, which is, in fact, the cornerstone of properly controlling and managing environmentally polluting gases.

You can freely access to our recently published paper using following link:
https://authors.elsevier.com/c/1gklk_LnESYZo9

Reinforcement Learning

May 8 - July 16


This course provides a comprehensive introduction to the field of reinforcement learning, from fundamentals to advanced techniques and applications. Through a combination of lectures, and coding and written assignments, you will learn about the core approaches and challenges in the field, including generalization and exploration. You will also have a chance to explore the concept of deep reinforcement learning—an extremely promising new area that combines reinforcement learning with deep learning techniques. By the end of the 10-week course, you will have a deep understanding of the theoretical foundations and practical applications of reinforcement learning in real-world scenarios.
Topics Include:
Dynamic programming
Monte Carlo tree search
Monte Carlo methods and temporal difference learning
Policy gradient methods
RL with value function approximation
Batch and offline reinforcement learning

Predicting uniaxial compressive strength from drilling variables aided by hybrid machine learning

Abstract
Awareness of uniaxial compressive strength (UCS) as a key rock formation parameter for the design and development of gas and oil field plays. It plays an essential role in the selection of the drill bits and stability of the wellbore’s wall. Precise prediction of UCS before or during the drilling, especially in exploration wellbores, is necessary to improve the drilling speed and reduce the instability of the wellbore walls. UCS predictor machine-learning (ML) models are developed in this study using drilling parameters recorded during drilling using least-squares support-vector machine (LSSVM) and multi-layer extreme learning machine (MELM) algorithms hybridized with cuckoo optimization algorithm (COA), particle swarm optimization (PSO) and genetic algorithm (GA) optimizers. In addition, stand-alone LSSVM and convolutional neural network (CNN) models without optimizer enhancements are evaluated. Drilling and petrophysical data recorded for two wells (A and B) from the Rag-e-Safid oil field in southwest Iran were compiled to form the studied dataset. UCS was initially calculated numerically based on data from laboratory tests from petrophysical logs. The Well A dataset was pre-processed to remove outlying data records by applying the quantile regression algorithm. That analysis indicated that 9 data records should be removed from the Well A dataset. A decision tree model was employed for feature selection purposes to identify the more influential variables with respect to UCS. Depth, weight on the drill bit (WOB), drill-string rotation speed (RPM), rate of penetration (ROP), and torque (Trq) were the variables identified as being highly influential on UCS values. Application of the ML models on the training data subset (75% of Well A data records) revealed that the MELM-COA algorithm achieved the lowest root mean squared error (4.6945 MPa) and a higher coefficient of determination (0.9873) value than the other models when predicting UCS in the Well A training and validation data subsets. The Well-A-trained MELM-COA model confirmed its generalizability within the studied field by generating low UCS prediction errors when applied to the independent Well B testing dataset.

You can freely download this paper from following link:
https://authors.elsevier.com/c/1hR3C4sPjBu7LS

Dear esteemed members of the channel,

If you have a strong passion for participating in research projects, we kindly request you to contact the channel management. It is essential that you possess prior experience in research and article publication, along with proficient English writing skills, to be eligible for this collaboration.

We anxiously await your response and look forward to a productive partnership aimed at achieving remarkable outcomes.

@mmehrad1986