Machine Learning Prediction of Formation Evaluation Logs in the Gulf of Mexico

2021 ◽  
Author(s):  
Brian LeCompte ◽  
Tosin Majekodunmi ◽  
Mike Staines ◽  
Gareth Taylor ◽  
Barry Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Gulf Of Mexico ◽  
Real Time ◽  
Network Architecture ◽  
Gamma Ray ◽  
Risk Mitigation ◽  
Seismic Inversion ◽  
Log Data ◽  
Formation Evaluation

Abstract The objective of the paper is to describe the application of artificial intelligence software to predict formation evaluation logs (compressional sonic, shear sonic and density) using only gamma ray, and resistivity log data and drilling dynamics data as received by the electronic drilling recorder (EDR). The software was applied real-time as a well was being drilled in deepwater Gulf of Mexico. Thorough examination and conditioning of EDR and wireline data give way to a training model construction for the artificial neural network (ANN) using full suites of log-data in offset wells. Next, a neural network architecture and associated hyperparameters are chosen and tested. The fully trained and validated model is applied to the gamma ray, resistivity and EDR of the target well while drilling. Real-time EDR and wireline data flow via WITSML from rig to cloud and data is delivered to the client. The results of the study indicate the simulated log data were comparable to those measured from conventional logging tools over the study area. In both blind well tests the density agreed with the conventional log results within 1.1 % and the compressional within 2.51 % (Figure 1). Each of these is well within the range of variance expected of repeat runs of a conventional logging tool. A primary driver for near real-time logs was to confirm structural depth of the target sands along the well bore. There was a depleted sand below the expected TD of the well that, if encountered, could have led to total losses and possible loss of the wellbore. It was critical to have real-time logs to characterize the sands above the depleted sand, using every possible petrophysical and geologic character to refine the log correlation. This integration of all the logs provided the best interpretation of the sand quality and led toward the completion decision. AI-based logs are a highly cost-effective alternative to LWD logging. It presents an environmentally friendly approach as there is no logging personnel on-site and no expensive and potentially dangerous nuclear sources in the hole The deployment of this patented, machine learning-driven, real-time simulation of formation evaluation logs is unique in using only gamma ray, resistivity and drilling data. It is particularly useful in the overburden section where formation evaluation tools are often not run for cost reasons, in side-tracks, in HP/HT settings and operational risk mitigation. It provides additive data for other petrophysical/QI/rock property analyses including seismic inversion, shale content, porosity, log QC/editing, real-time LWD, drilling optimization, etc.

Real Time Cloud-Based Automation for Formation Evaluation Optimization, Risk Mitigation and Decarbonization

2021 ◽  
Author(s):  
Rebecca Nye ◽  
Camilo Mejia ◽  
Evgeniya Dontsova
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Risk Mitigation ◽  
High Rate ◽  
Learning Models ◽  
Sample Collection ◽  
Formation Evaluation ◽  
The Real ◽  
Prediction Techniques ◽  
Machine Learning Models

Abstract Recent developments in artificial intelligence (AI) have enabled upstream exploration and production companies to make better, faster and accurate decisions at any stage of well construction, while reducing operational expenditure and risk, increasing logistic efficiencies. The achieved optimization through digitization at the wellsite will significantly reduce the carbon emissions per well drilled when fully embraced by the industry. In addition, an industry pushed to drill in more challenging environments, they must embrace safer and more practical methods. An increase in prediction techniques, to generate synthetic formation evaluation wellbore logs, has unlocked the ability to implement a combination of predictive and prescriptive analytics with petrophysical and geochemical workflows in real time. The foundation of the real time automation is based on advanced machine learning (ML) techniques that are deployed via cloud connectivity. Three levels of logging precision are defined in the automated workflow based on the data inputs and machine learning models. The first level is the forecasting ahead of the bit that implements advanced machine learning using historical data, aiding proactive operational decisions. The second level has improved precision by incorporating real time drilling measurements and providing a credible contingency to for wellbore logging program. The last level incorporates petrophysical workflows and geochemical measurements to achieve the highest precision for logging prediction in the industry. Supervised and unsupervised machine learning models are presented to demonstrate the path for automation. Precision above 95% in the real time automated workflows was achieved with a combination of physics and advanced machine learning models. The automation of the workflow has assisted with optimization of logging programs utilizing technology with costly lost in hole charges and high rate of tool failures in offshore operations. The optimization has reduced the requirement for logistics associated with logging and eliminated the need for radioactive sources and lithium batteries. Highest precision in logging prediction has been achieved through an automated workflow for real time operations. In addition, the workflow can also be deployed with robotics technology to automate sample collection, leading to increased efficiencies.

scholarly journals Robust Approach to Supervised Deep Neural Network Training for Real-Time Object Classification in Cluttered Indoor Environment

2021 ◽  
Vol 11 (15) ◽  
pp. 7148
Author(s):  
Bedada Endale ◽  
Abera Tullu ◽  
Hayoung Shi ◽  
Beom-Soo Kang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Input Data ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Object Classification ◽  
Training Data ◽  
Gradient Descent Algorithm

Unmanned aerial vehicles (UAVs) are being widely utilized for various missions: in both civilian and military sectors. Many of these missions demand UAVs to acquire artificial intelligence about the environments they are navigating in. This perception can be realized by training a computing machine to classify objects in the environment. One of the well known machine training approaches is supervised deep learning, which enables a machine to classify objects. However, supervised deep learning comes with huge sacrifice in terms of time and computational resources. Collecting big input data, pre-training processes, such as labeling training data, and the need for a high performance computer for training are some of the challenges that supervised deep learning poses. To address these setbacks, this study proposes mission specific input data augmentation techniques and the design of light-weight deep neural network architecture that is capable of real-time object classification. Semi-direct visual odometry (SVO) data of augmented images are used to train the network for object classification. Ten classes of 10,000 different images in each class were used as input data where 80% were for training the network and the remaining 20% were used for network validation. For the optimization of the designed deep neural network, a sequential gradient descent algorithm was implemented. This algorithm has the advantage of handling redundancy in the data more efficiently than other algorithms.

Automating Well Log Correlation Workflow Using Soft Attention Convolutional Neural Networks

2021 ◽  
Author(s):  
Aria Abubakar ◽  
Mandar Kulkarni ◽  
Anisha Kaul
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Gamma Ray ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Well Log ◽  
Machine Learning Model ◽  
Well Correlation ◽  
Geological Formations

Abstract In the process of deriving the reservoir petrophysical properties of a basin, identifying the pay capability of wells by interpreting various geological formations is key. Currently, this process is facilitated and preceded by well log correlation, which involves petrophysicists and geologists examining multiple raw log measurements for the well in question, indicating geological markers of formation changes and correlating them with those of neighboring wells. As it may seem, this activity of picking markers of a well is performed manually and the process of ‘examining’ may be highly subjective, thus, prone to inconsistencies. In our work, we propose to automate the well correlation workflow by using a Soft- Attention Convolutional Neural Network to predict well markers. The machine learning algorithm is supervised by examples of manual marker picks and their corresponding occurrence in logs such as gamma-ray, resistivity and density. Our experiments have shown that, specifically, the attention mechanism allows the Convolutional Neural Network to look at relevant features or patterns in the log measurements that suggest a change in formation, making the machine learning model highly precise.

scholarly journals Machine-learning in astronomy

2014 ◽  
Vol 10 (S306) ◽  
pp. 279-287 ◽  
Author(s):  
Michael Hobson ◽  
Philip Graff ◽  
Farhan Feroz ◽  
Anthony Lasenby
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Gamma Ray ◽  
Neural Network Training ◽  
Training Algorithm ◽  
Data Description ◽  
Astronomical Data ◽  
Machine Learning Methods ◽  
Network Training

AbstractMachine-learning methods may be used to perform many tasks required in the analysis of astronomical data, including: data description and interpretation, pattern recognition, prediction, classification, compression, inference and many more. An intuitive and well-established approach to machine learning is the use of artificial neural networks (NNs), which consist of a group of interconnected nodes, each of which processes information that it receives and then passes this product on to other nodes via weighted connections. In particular, I discuss the first public release of the generic neural network training algorithm, calledSkyNet, and demonstrate its application to astronomical problems focusing on its use in the BAMBI package for accelerated Bayesian inference in cosmology, and the identification of gamma-ray bursters. TheSkyNetand BAMBI packages, which are fully parallelised using MPI, are available athttp://www.mrao.cam.ac.uk/software/.

Traditional versus Neural Network Classification Methods for Facial Emotion Recognition

2021 ◽  
Vol 7 (2) ◽  
pp. 203-206
Author(s):  
Herag Arabian ◽  
Verena Wagner-Hartl ◽  
Knut Moeller
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Emotion Recognition ◽  
Network Architecture ◽  
Autism Spectrum ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Machine Model

Abstract Facial emotion recognition (FER) is a topic that has gained interest over the years for its role in bridging the gap between Human and Machine interactions. This study explores the potential of real time FER modelling, to be integrated in a closed loop system, to help in treatment of children suffering from Autism Spectrum Disorder (ASD). The aim of this study is to show the differences between implementing Traditional machine learning and Deep learning approaches for FER modelling. Two classification approaches were taken, the first approach was based on classic machine learning techniques using Histogram of Oriented Gradients (HOG) for feature extraction, with a k-Nearest Neighbor and a Support Vector Machine model as classifiers. The second approach uses Transfer Learning based on the popular “Alex Net” Neural Network architecture. The performance of the approaches was based on the accuracy of randomly selected validation sets after training on random training sets of the Oulu-CASIA database. The data analyzed shows that traditional machine learning methods are as effective as deep neural net models and are a good compromise between accuracy, extracted features, computational speed and costs.

PREDICTION AND ANALYSIS OF GEOMECHANICAL PROPERTIES OF JIMUSAER SHALE USING A MACHINE LEARNING APPROACH

2021 ◽  
Author(s):  
Lianteng Song ◽  
◽  
Zhonghua Liu ◽  
Chaoliu Li ◽  
Congqian Ning ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Gamma Ray ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Sequential Data ◽  
Log Data ◽  
Geomechanical Properties ◽  
Single Well

Geomechanical properties are essential for safe drilling, successful completion, and exploration of both conven-tional and unconventional reservoirs, e.g. deep shale gas and shale oil. Typically, these properties could be calcu-lated from sonic logs. However, in shale reservoirs, it is time-consuming and challenging to obtain reliable log-ging data due to borehole complexity and lacking of in-formation, which often results in log deficiency and high recovery cost of incomplete datasets. In this work, we propose the bidirectional long short-term memory (BiL-STM) which is a supervised neural network algorithm that has been widely used in sequential data-based pre-diction to estimate geomechanical parameters. The pre-diction from log data can be conducted from two differ-ent aspects. 1) Single-Well prediction, the log data from a single well is divided into training data and testing data for cross validation; 2) Cross-Well prediction, a group of wells from the same geographical region are divided into training set and testing set for cross validation, as well. The logs used in this work were collected from 11 wells from Jimusaer Shale, which includes gamma ray, bulk density, resistivity, and etc. We employed 5 vari-ous machine learning algorithms for comparison, among which BiLSTM showed the best performance with an R-squared of more than 90% and an RMSE of less than 10. The predicted results can be directly used to calcu-late geomechanical properties, of which accuracy is also improved in contrast to conventional methods.

Enhancing Vibration-Based Structural Health Monitoring via Edge Computing: A Tiny Machine Learning Perspective

2021 ◽  
Author(s):  
Federica Zonzini ◽  
Francesca Romano ◽  
Antonio Carbone ◽  
Matteo Zauli ◽  
Luca De Marchi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Network Architecture ◽  
Binary Classification ◽  
Low Cost ◽  
Classification Problems ◽  
Computationally Efficient ◽  
Structural Health

Abstract Despite the outstanding improvements achieved by artificial intelligence in the Structural Health Monitoring (SHM) field, some challenges need to be coped with. Among them, the necessity to reduce the complexity of the models and the data-to-user latency time which are still affecting state-of-the-art solutions. This is due to the continuous forwarding of a huge amount of data to centralized servers, where the inference process is usually executed in a bulky manner. Conversely, the emerging field of Tiny Machine Learning (TinyML), promoted by the recent advancements by the electronic and information engineering community, made sensor-near data inference a tangible, low-cost and computationally efficient alternative. In line with this observation, this work explored the embodiment of the One Class Classifier Neural Network, i.e., a neural network architecture solving binary classification problems for vibration-based SHM scenarios, into a resource-constrained device. To this end, OCCNN has been ported on the Arduino Nano 33 BLE Sense platform and validated with experimental data from the Z24 bridge use case, reaching an average accuracy and precision of 95% and 94%, respectively.

scholarly journals Real-time diameter of the fetal aorta from ultrasound

2019 ◽  
Vol 32 (11) ◽  
pp. 6735-6744
Author(s):  
Nicoló Savioli ◽  
Enrico Grisan ◽  
Silvia Visentin ◽  
Erich Cosmi ◽  
Giovanni Montana ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Architecture ◽  
Mean Squared Error ◽  
Ultrasound Images ◽  
Imaging Features ◽  
Execution Speed ◽  
The Mean ◽  
Temporal Redundancy ◽  
Gated Recurrent Unit

AbstractThe automatic analysis of ultrasound sequences can substantially improve the efficiency of clinical diagnosis. This article presents an attempt to automate the challenging task of measuring the vascular diameter of the fetal abdominal aorta from ultrasound images. We propose a neural network architecture consisting of three blocks: a convolutional neural network (CNN) for the extraction of imaging features, a convolution gated recurrent unit (C-GRU) for exploiting the temporal redundancy of the signal, and a regularized loss function, called CyclicLoss, to impose our prior knowledge about the periodicity of the observed signal. The solution is investigated with a cohort of 25 ultrasound sequences acquired during the third-trimester pregnancy check, and with 1000 synthetic sequences. In the extraction of features, it is shown that a shallow CNN outperforms two other deep CNNs with both the real and synthetic cohorts, suggesting that echocardiographic features are optimally captured by a reduced number of CNN layers. The proposed architecture, working with the shallow CNN, reaches an accuracy substantially superior to previously reported methods, providing an average reduction of the mean squared error from 0.31 (state-of-the-art) to 0.09 $$\mathrm{mm}^2$$mm2, and a relative error reduction from 8.1 to 5.3%. The mean execution speed of the proposed approach of 289 frames per second makes it suitable for real-time clinical use.

scholarly journals Efficient Semantic Segmentation Using Multi-Path Decoder

2020 ◽  
Vol 10 (18) ◽  
pp. 6386
Author(s):  
Xing Bai ◽  
Jun Zhou
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Architecture ◽  
Resource Constraints ◽  
Cost Effective ◽  
Semantic Segmentation ◽  
Classification Model ◽  
Neural Network Architecture ◽  
Great Progress ◽  
Different Types

Benefiting from the booming of deep learning, the state-of-the-art models achieved great progress. But they are huge in terms of parameters and floating point operations, which makes it hard to apply them to real-time applications. In this paper, we propose a novel deep neural network architecture, named MPDNet, for fast and efficient semantic segmentation under resource constraints. First, we use a light-weight classification model pretrained on ImageNet as the encoder. Second, we use a cost-effective upsampling datapath to restore prediction resolution and convert features for classification into features for segmentation. Finally, we propose to use a multi-path decoder to extract different types of features, which are not ideal to process inside only one convolutional neural network. The experimental results of our model outperform other models aiming at real-time semantic segmentation on Cityscapes. Based on our proposed MPDNet, we achieve 76.7% mean IoU on Cityscapes test set with only 118.84GFLOPs and achieves 37.6 Hz on 768 × 1536 images on a standard GPU.

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