Predict Geomechanical Parameters with Machine Learning Combining Drilling Data and Gamma Ray

2021 ◽  
Author(s):  
Mattia Martinelli ◽  
Ivo Colombo ◽  
Eliana Rosa Russo
Keyword(s):  
Machine Learning ◽  
Bulk Density ◽  
Young’S Modulus ◽  
Gamma Ray ◽  
Young's Modulus ◽  
Geomechanical Parameters ◽  
Drilling Parameters ◽  
Unseen Data ◽  
Drilling Data ◽  
Sonic Logs

Abstract The aim of this work is the development of a fast and reliable method for geomechanical parameters evaluation while drilling using surface logging data. Geomechanical parameters are usually evaluated from cores or sonic logs, which are typically expensive and sometimes difficult to obtain. A novel approach is here proposed, where machine learning algorithms are used to calculate the Young's Modulus from drilling parameters and the gamma ray log. The proposed method combines typical mud logging drilling data (ROP, RPM, Torque, Flow measurements, WOB and SPP), XRF data and well log data (Sonic logs, Bulk Density, Gamma Ray) with several machine learning techniques. The models were trained and tested on data coming from three wells drilled in the same basin in Kuwait, in the same geological units but in different reservoirs. Sonic logs and bulk density are used to evaluate the geomechanical parameters (e.g. Young's Modulus) and to train the model. The training phase and the hyperparameter tuning were performed using data coming from a single well. The model was then tested against previously unseen data coming from the other two wells. The trained model is able to predict the Young's modulus in the test wells with a root mean squared error around 12 GPa. The example here provided demonstrates that a model trained with drilling parameters and gamma ray coming from one well is able to predict the Young Modulus of different wells in the same basin. These outcomes highlight the potentiality of this procedure and point out several implications for the reservoir characterization. Indeed, once the model has been trained, it is possible to predict the Young's Modulus in different wells of the same basin using only surface logging data.

Drilling Data-Based Approach to Build a Continuous Static Elastic Moduli Profile Utilizing Artificial Intelligence Techniques

2021 ◽  
pp. 1-13
Author(s):  
Osama Siddig ◽  
Saad Alafnan ◽  
Salaheldin Elkatatny ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Young’S Modulus ◽  
Elastic Moduli ◽  
Young's Modulus ◽  
Machine Learning Algorithms ◽  
Inference System ◽  
Artificial Intelligence Techniques ◽  
Drilling Rig ◽  
Drilling Data

Abstract Young's modulus is a principle geomechanical property that reflects the material stiffness. Good knowledge about rock mechanical properties significantly facilitates fracturing design and in-situ stresses estimation. Conventionally, rock elastic properties are estimated either experimentally or using well log data, known as static and dynamic respectively. Conducting experiments on core samples is costly, time-consuming and does not provide continuous information. While dynamic Young's modulus provides a complete profile, however, it needs the availability of acoustic logs and its estimations differ from the static values. The objective of this paper is to create a continuous profile of Young's modulus using the drilling rig sensors records. The presented approach relies on the fact that the drilling data such as drill pipe torque, weight on bit and rate of penetration are available at an early stage without additional cost. Three machine-learning algorithms were used to correlate the drilling data with Young's modulus: random forest, adaptive neuro-fuzzy inference system and functional network. Two different datasets were used in this study, one construct and test the model, while the other was hidden from the algorithms and used later to validate the built models. The two datasets contain over 3900 data points and cover different types of rocks. Two out of the three methods utilized yielded a remarkable match between the given and predicted values. The correlation coefficients ranged between 0.92 and 0.99 average absolute percentage errors were less than 13%. Supported by these results, the utilization of drilling data and artificial intelligence techniques to predict the elastic moduli is promising. This approach could be investigated for other geomechanical properties, besides, the performance of other machine-learning methods for the same purpose.

Application of Machine Learning for Lithology-on-Bit Prediction using Drilling Data in Real-Time

2021 ◽  
Author(s):  
Temirlan Zhekenov ◽  
Artem Nechaev ◽  
Kamilla Chettykbayeva ◽  
Alexey Zinovyev ◽  
German Sardarov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Hybrid Modeling ◽  
Data Set ◽  
Drilling Parameters ◽  
Drilling Data ◽  
Stable Solutions ◽  
Mud Logging

SUMMARY Researchers base their analysis on basic drilling parameters obtained during mud logging and demonstrate impressive results. However, due to limitations imposed by data quality often present during drilling, those solutions often tend to lose their stability and high levels of predictivity. In this work, the concept of hybrid modeling was introduced which allows to integrate the analytical correlations with algorithms of machine learning for obtaining stable solutions consistent from one data set to another.

scholarly journals Application of Machine Learning in Evaluation of the Static Young’s Modulus for Sandstone Formations

2020 ◽  
Vol 12 (5) ◽  
pp. 1880 ◽  
Author(s):  
Ahmed Abdulhamid Mahmoud ◽  
Salaheldin Elkatatny ◽  
Dhafer Al Shehri
Keyword(s):  
Machine Learning ◽  
Young’S Modulus ◽  
Transit Time ◽  
Young's Modulus ◽  
Wellbore Stability ◽  
Learning Models ◽  
Static Young’S Modulus ◽  
Data Points ◽  
Sandstone Formation ◽  
Machine Learning Models

Prediction of the mechanical characteristics of the reservoir formations, such as static Young’s modulus (Estatic), is very important for the evaluation of the wellbore stability and development of the earth geomechanical model. Estatic considerably varies with the change in the lithology. Therefore, a robust model for Estatic prediction is needed. In this study, the predictability of Estatic for sandstone formation using four machine learning models was evaluated. The design parameters of the machine learning models were optimized to improve their predictability. The machine learning models were trained to estimate Estatic based on bulk formation density, compressional transit time, and shear transit time. The machine learning models were trained and tested using 592 well log data points and their corresponding core-derived Estatic values collected from one sandstone formation in well-A and then validated on 38 data points collected from a sandstone formation in well-B. Among the machine learning models developed in this work, Mamdani fuzzy interference system was the highly accurate model to predict Estatic for the validation data with an average absolute percentage error of only 1.56% and R of 0.999. The developed static Young’s modulus prediction models could help the new generation to characterize the formation rock with less cost and safe operation.

A Convenient Technology to Calculate Geomechanical Properties From Drilling Data

2020 ◽  
Author(s):  
Mazeda Tahmeen ◽  
Geir Hareland ◽  
John P. Hayes
Keyword(s):  
Young’S Modulus ◽  
Poisson’S Ratio ◽  
Young's Modulus ◽  
Poisson's Ratio ◽  
Brittleness Index ◽  
Eagle Ford ◽  
Horizontal Drilling ◽  
Geomechanical Properties ◽  
Drilling Data ◽  
Rock Brittleness

Abstract The multistage hydraulic fracturing is the best practice to stimulate unconventional hydrocarbon reservoirs for optimal production. Recent studies suggested that selective stimulation design could significantly increase production rates at a reduced cost rather than using non-selective geometric stages. An optimal design needs detailed logging and core information to selectively perforate and optimize the stimulation treatment. In most cases, the non-selective evenly spaced geometric stimulation design is used, primarily due to the time consuming and expensive conventional logging tools and techniques. In this article, a 3D wellbore friction model is used to estimate the effective downhole weight on bit (DWOB) from the drilling data, directional survey data and drill string information. The estimated DWOB is used as an input to the inverted rate of penetration (ROP) model along with other drilling data, drill bit specifications and reservoir specific formation constants, to calculate rock mechanical and reservoir properties including, compressive strength, Young’s modulus, porosity, permeability and Poisson’s ratio without the use of expensive downhole logging tools. The rock brittleness index is calculated from the relationship between Young’s modulus and Poisson’s ratio based on the definitions of rock brittleness used in recent years. The field data from horizontal drilling of three sample wells were used to investigate the geomechanical properties in the Montney shale formation and the lower Eagle Ford formation in North America. The calculated geomechanical properties were compared to the corresponding test analysis on cores. The authors investigated the rock brittleness index from the sample well data drilled horizontally in the lower Eagle Ford formation. This novel technology could help geologists and reservoir engineers better exploit unconventional reservoirs leading to optimal selective stimulations and greater net present value (NPV).

Boehmite Nanofiber-Reinforced Resorcinol-Formaldehyde Macroporous Monoliths for Heat/Flame Protection

2018 ◽  
Author(s):  
Gen Hayase
Keyword(s):  
Phase Separation ◽  
Bulk Density ◽  
Young’S Modulus ◽  
Uniaxial Compression ◽  
Young's Modulus ◽  
Flame Resistance ◽  
Resorcinol Formaldehyde ◽  
Insulating Properties ◽  
Flame Protection

By distributing boehmite nanofibers (BNFs) to a resorcinol-formaldehyde (RF) skeletal phase formed by phase separation in an aqueous sol, composite macroporous monoliths have been produced. The nanofiber reinforced monoliths have a skeleton in which BNF is arranged in parallel within the RF structure, and showed high Young's modulus against uniaxial compression for their bulk density. These materials can be expected to be applied to heat/flame protection materials using heat insulating properties and high flame resistance.<br>

Fracture Height Prediction Model Utilizing Openhole Logs, Mechanical Models, and Temperature Cooldown Analysis with Machine Learning Algorithms

2021 ◽  
Author(s):  
Abdul Muqtadir Khan ◽  
Abdullah BinZiad ◽  
Abdullah Al Subaii ◽  
Denis Bannikov ◽  
Maksim Ponomarev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bulk Density ◽  
Gamma Ray ◽  
Rock Type ◽  
Operational Efficiency ◽  
Height Growth ◽  
Machine Learning Algorithms ◽  
Successful Implementation ◽  
Net Pressure ◽  
Fracture Height

Abstract Vertical wells require diagnostic techniques after minifrac pumping to interpret fracture height growth. This interpretation provides vital input to hydraulic fracturing redesign workflows. The temperature log is the most widely used technique to determine fracture height through cooldown analysis. A data science approach is proposed to leverage available measurements, automate the interpretation process, and enhance operational efficiency while keeping confidence in the fracturing design. Data from 55 wells were ingested to establish proof of concept.The selected geomechanical rock texture parameters were based on the fracturing theory of net-pressure-controlled height growth. Interpreted fracture height from input temperature cooldown analysis was merged with the structured dataset. The dataset was constructed at a high vertical depth of resolution of 0.5 to 1 ft. Openhole log data such as gamma-ray and bulk density helped to characterize the rock type, and calculated mechanical properties from acoustic logs such as in-situ stress and Young's modulus characterize the fracture geometry development. Moreover, injection rate, volume, and net pressure during the calibration treatment affect the fracture height growth. A machine learning (ML) workflow was applied to multiple openhole log parameters, which were integrated with minifrac calibration parameters along with the varying depth of the reservoir. The 55 wells datasets with a cumulative 120,000 rows were divided into training and testing with a ratio of 80:20. A comparative algorithm study was conducted on the test set with nine algorithms, and CatBoost showed the best results with an RMSE of 4.13 followed by Random Forest with 4.25. CatBoost models utilize both categorical and numerical data. Stress, gamma-ray, and bulk density parameters affected the fracture height analyzed from the post-fracturing temperature logs. Following successful implementation in the pilot phase, the model can be extended to horizontal wells to validate predictions from commercial simulators where stress calculations were unreliable or where stress did not entirely reflect changes in rock type. By coupling the geometry measurement technology with data analysis, a useful automated model was successfully developed to enhance operational efficiency without compromising any part of the workflow. The advanced algorithm can be used in any field where precise fracture placement of a hydraulic fracture contributes directly to production potential. Also, the model can play a critical role in cube development to optimize lateral landing and lateral density for exploration fields.

scholarly journals Predicting Young's modulus of oxide glasses with sparse datasets using machine learning

2019 ◽  
Vol 524 ◽  
pp. 119643 ◽  
Author(s):  
Suresh Bishnoi ◽  
Sourabh Singh ◽  
R. Ravinder ◽  
Mathieu Bauchy ◽  
Nitya Nand Gosvami ◽  
...  
Keyword(s):  
Machine Learning ◽  
Young’S Modulus ◽  
Young's Modulus ◽  
Oxide Glasses

scholarly journals Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hrishabh Khakurel ◽  
M. F. N. Taufique ◽  
Ankit Roy ◽  
Ganesh Balasubramanian ◽  
Gaoyuan Ouyang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Young’S Modulus ◽  
Young's Modulus ◽  
Atomic Radius ◽  
Absolute Error ◽  
Gradient Boosting ◽  
Valence Electron Concentration ◽  
Validation Data ◽  
Average Valence ◽  
The Difference

AbstractWe identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young’s modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young’s modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications.

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