Automated Carbonate Reservoir Pore and Fracture Classification by Multiscale Imaging and Deep Learning

2021 ◽  
Author(s):  
J. Klaver ◽  
J. Schmatz ◽  
R. Wang ◽  
M. Jiang ◽  
L.M. Kleipool ◽  
...  
Keyword(s):  
Deep Learning ◽  
Carbonate Reservoir ◽  
Fracture Classification ◽  
Multiscale Imaging

Sparse Water Fracture Channel Detection from Subsurface Sensors Via a Smart Orthogonal Matching Pursuit

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
Abdallah Al Shehri ◽  
Alberto Marsala
Keyword(s):  
Deep Learning ◽  
Monitoring System ◽  
Water Saturation ◽  
Matching Pursuit ◽  
Base Station ◽  
Carbonate Reservoir ◽  
Subsurface Reservoir ◽  
Front Movement ◽  
Water Front

Abstract Water front movement in fractured carbonate reservoirs occurs in micro-fractures, corridors and interconnected fracture channels (above 5 mm in size) that penetrate the carbonate reservoir structure. Determining the fracture channels and the water front movements within the flow corridors is critical to optimize sweep efficiency and increase hydrocarbon recovery. In this work, we present a new smart orthogonal matching pursuit (OMP) algorithm for water front movement detection in carbonate fracture channels. The method utilizes a combined artificial intelligence) AI-OMP approach to first analyze and extract the potential fracture channels and then subsequently deploys a deep learning approach for estimating the water saturation patterns in the fracture channels. The OMP utilizes the sparse fracture to sensor correlation to determine the fracture channels impacting each individual sensor. The deep learning method then utilizes the fracture channel estimates to assess the water front movements. We tested the AI-OMP framework on a synthetic fracture carbonate reservoir box model exhibiting a complex fracture system. Fracture Robots (FracBots, about 5mm in size) technology will be used to sense key reservoir parameters (e.g., temperature, pressure, pH and other chemical parameters) and represent an important step towards enhancing reservoir surveillance (Al Shehri, et al. 2021). The technology is comprised of a wireless micro-sensor network for mapping and monitoring fracture channels in conventional and unconventional reservoirs. The system establishes wireless network connectivity via magnetic induction (MI)-based communication, since it exhibits highly reliable and constant channel conditions with sufficiently communication range inside an oil reservoir environment. The system architecture of the FracBots network has two layers: FracBot nodes layer and a base station layer. A number of subsurface FracBot sensors are injected in the formation fracture channels to record data affected by changes in water saturation. The sensor placement can be adapted in the reservoir formation in order to improve sensor measurement data quality, as well as better track the penetrating water fronts. They will move with the injected fluids and distribute themselves in the fracture channels where they start sensing the surrounding environment’s conditions; they communicate the data, including their location coordinates, among each other to finally transmit the information in multi-hop fashion to the base station installed inside the wellbore. The base station layer consists of a large antenna connected to an aboveground gateway. The data collected from the FracBots network are transmitted to the control room via aboveground gateway for further processing. The results exhibited strong estimation performance in both accurately determining the fracture channels and the saturation pattern in the subsurface reservoir. The results indicate that the framework performs well; especially for fracture channels that are rather shallow (about 20 m from the wellbore) with significant changes in the saturation levels. This makes the in-situ reservoir sensing a viable permanent reservoir monitoring system for the tracking of fluid fronts, and determination of fracture channels. The novel framework presents a vital component in the data analysis and interpretation of subsurface reservoir monitoring system of fracture channels flow in carbonate reservoirs. The results outline the capability of in-situ reservoir sensors to deliver accurate tracking water-fronts and fracture channels in order to optimize recovery.

scholarly journals X-Ray Bone Fracture Classification Using Deep Learning: A Baseline for Designing a Reliable Approach

2020 ◽  
Vol 10 (4) ◽  
pp. 1507 ◽  
Author(s):  
Leonardo Tanzi ◽  
Enrico Vezzetti ◽  
Rodrigo Moreno ◽  
Sandro Moos
Keyword(s):  
Deep Learning ◽  
Bone Fracture ◽  
Bone Fractures ◽  
Area Under The Curve ◽  
Test Accuracy ◽  
Fracture Classification ◽  
Fracture Detection ◽  
Cad System ◽  
Dataset Size ◽  
Sensitivity Specificity

In recent years, bone fracture detection and classification has been a widely discussed topic and many researchers have proposed different methods to tackle this problem. Despite this, a universal approach able to classify all the fractures in the human body has not yet been defined. We aim to analyze and evaluate a selection of papers, chosen according to their representative approach, where the authors applied different deep learning techniques to classify bone fractures, in order to select the strengths of each of them and try to delineate a generalized strategy. Each study is summarized and evaluated using a radar graph with six values: area under the curve (AUC), test accuracy, sensitivity, specificity, dataset size and labelling reliability. Plus, we defined the key points which should be taken into account when trying to accomplish this purpose and we compared each study with our baseline. In recent years, deep learning and, in particular, the convolution neural network (CNN), has achieved results comparable to those of humans in bone fracture classification. Adopting a correct generalization, we are reasonably sure that a computer-aided diagnosis (CAD) system, correctly designed to assist doctors, would save a considerable amount of time and would limit the number of wrong diagnoses.

Hierarchical fracture classification of proximal femur X-Ray images using a multistage Deep Learning approach

2020 ◽  
Vol 133 ◽  
pp. 109373 ◽  
Author(s):  
Leonardo Tanzi ◽  
Enrico Vezzetti ◽  
Rodrigo Moreno ◽  
Alessandro Aprato ◽  
Andrea Audisio ◽  
...  
Keyword(s):  
Deep Learning ◽  
Proximal Femur ◽  
Learning Approach ◽  
Fracture Classification ◽  
X Ray

scholarly journals The application of deep learning algorithms to classify subsurface drilling lost circulation severity in large oil field datasets

2021 ◽  
Vol 3 (9) ◽  
Author(s):  
Sajjad Mardanirad ◽  
David A. Wood ◽  
Hassan Zakeri
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Carbonate Reservoir ◽  
Oil Field ◽  
Superior Performance ◽  
Large Field ◽  
Fluid Loss ◽  
List Type ◽  
Lost Circulation

Abstract In this paper, we present how precise deep learning algorithms can distinguish loss circulation severities in oil drilling operations. Lost circulation is one of the costliest downhole problem encountered during oil and gas well construction. Applying artificial intelligence can help drilling teams to be forewarned of pending lost circulation events and thereby mitigate their consequences. Data-driven methods are traditionally employed for fluid loss complexity quantification but are not able to achieve reliable predictions for field cases with large quantities of data. This paper attempts to investigate the performance of deep learning (DL) approach in classification the types of fluid loss from a very large field dataset. Three DL classification models are evaluated: Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Long-Short Term Memory (LSTM). Five fluid-loss classes are considered: No Loss, Seepage, Partial, Severe, and Complete Loss. 20 wells drilled into the giant Azadegan oil field (Iran) provide 65,376 data records are used to predict the fluid loss classes. The results obtained, based on multiple statistical performance measures, identify the CNN model as achieving superior performance (98% accuracy) compared to the LSTM and GRU models (94% accuracy). Confusion matrices provide further insight to the prediction accuracies achieved. The three DL models evaluated were all able to classify different types of lost circulation events with reasonable prediction accuracy. Future work is required to evaluate the performance of the DL approach proposed with additional large datasets. The proposed method helps drilling teams deal with lost circulation events efficiently. Article Highlights Three deep learning models classify fluid loss severity in an oil field carbonate reservoir. Deep learning algorithms advance machine learning a large resource dataset with 65,376 data records. Convolution neural network outperformed other deep learning methods.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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