A framework for predicting the production performance of unconventional resources using deep learning

2021 ◽  
Vol 295 ◽  
pp. 117016
Author(s):  
Sen Wang ◽  
Chaoxu Qin ◽  
Qihong Feng ◽  
Farzam Javadpour ◽  
Zhenhua Rui
Keyword(s):  
Deep Learning ◽  
Production Performance ◽  
Unconventional Resources

Investigating Transfer Learning for Characterization and Performance Prediction in Unconventional Reservoirs

2021 ◽  
Author(s):  
Jodel Cornelio ◽  
Syamil Mohd Razak ◽  
Atefeh Jahandideh ◽  
Behnam Jafarpour ◽  
Young Cho ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Method Development ◽  
Simulated Data ◽  
Production Performance ◽  
Unconventional Reservoirs ◽  
Eagle Ford ◽  
Input Parameters

Abstract Transfer learning is a machine learning concept whereby the knowledge gained (e.g., a model developed) in one task can be transferred (applied) to solve a different but related task. In the context of unconventional reservoirs, the concept can be used to transfer a machine learning model that is learned from data in one field (or shale play) to another, thereby significantly reducing the data needs and efforts to build a new model from scratch. In this work, we study the feasibility of developing deep learning models that can capture and transfer common features in a rich dataset pertaining to a mature unconventional play to enable production prediction in a new unconventional play with limited available data. The focus in this work is on method development using simulated data that correspond to the Bakken and Eagle Ford Shale Plays as two different unconventional plays in the US. We use formation and completion parameter ranges that correspond to the Bakken play with their simulated production responses to explore different approaches for training neural network models that enable transfer learning to predict production responses of input parameters corresponding to the Eagle Ford play (previously unseen input parameters). We explore different schemes by accessing the internal components of the model to extrapolate and categorize salient features that are represented in the trained neural network. Ultimately, our goal is to use these new mechanisms to enable effective sharing and reuse of discovered features from one unconventional well to another. To extract salient trends from formation and completion input parameters and their corresponding simulated production responses, we use deep learning architectures that consist of convolutional encoder-decoder networks. The architecture is then trained with rich simulated data from one field to generate a robust mapping between the input and the output feature spaces. The "learned" parameters from this network can then be "transferred" to develop a different predictive model for another field that may lack sufficient historical data. The results show that using standard training approaches, a neural network model that is trained with sufficiently large data samples from Bakken could produce reliable prediction models for typical wells that may be found in that field. The same neural network, however, could not produce reliable predictions for a typical Eagle Ford well. Furthermore, we observe that a neural network trained with insufficient data samples from Eagle Ford produces a poor prediction model for typical wells that may be found in Eagle Ford. However, when extrapolated feature components of the Bakken neural network were integrated into the training process of the Eagle Ford neural network, the resulting predictions for typical Eagle Ford wells improved significantly. Moreover, we observe that the ability to transfer learning can improve when specialized training strategies are adopted to enable transfer learning. Using several numerical experiments, the paper presents and assesses various transfer learning strategies to predict the production performance of unconventional wells in a new area with limited information by integrating knowledge from more mature plays.

scholarly journals The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep Learning

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Chaodong Tan ◽  
Song Wang ◽  
Hanwen Deng ◽  
Guoqing Han ◽  
Guanghao Du ◽  
...  
Keyword(s):  
Deep Learning ◽  
Health Status ◽  
Prediction Model ◽  
Coalbed Methane ◽  
Short Term Memory ◽  
Production Performance ◽  
Health Index ◽  
Step Method ◽  
Artificial Lift ◽  
Status Assessment

Aiming at the problems of the current production and operation status of the progressive cavity pump (PCP) in coalbed methane (CBM) wells which cannot be timely monitored, quantitatively evaluated, and accurately predicted, a five-step method for evaluating and predicting the health status of PCP wells is proposed: data preprocessing, principal parameter optimization, health index construction, health degree division, and health index prediction. Therein, a health index (HI) formulation was made based on deep learning, and a statistical method was used to define the health status of PCP wells as being healthy, subhealthy, or faulty. This allowed further research on the HI prediction model of PCP wells based on the long short-term memory (LSTM) network. As demonstrated in the study, they can reflect both the change trend and the contextual relevance of the health status of PCP wells with high accuracy to achieve real-time, quantitative, and accurate assessment and prediction. At the same time, the conclusion gives good guidance on the production performance analysis and failure warning of the PCP wells and suggests a new direction for the health status assessment and warning of other artificial lift equipment.

Production Performance in the In-Fill Development of Unconventional Resources

2015 ◽  
Author(s):  
Bilu V. Cherian ◽  
Matthew McCleary ◽  
Samuel Fluckiger ◽  
Nathan Nieswiadomy ◽  
Brent Bundy ◽  
...  
Keyword(s):  
Production Performance ◽  
Unconventional Resources

A Robust Predictive Machine Learning Model for Supercritical CO2 Foam Strength with Integrated Testing Parameters

2021 ◽  
Author(s):  
Shehzad Ahmed ◽  
Waleed Alameri ◽  
Waqas Waseem Ahmed ◽  
Alvinda Sri Hanamertani ◽  
Sameer Ahmed Khan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Flow Behavior ◽  
Polymer Concentration ◽  
Operating Conditions ◽  
Production Performance ◽  
Foaming Properties ◽  
Process Variables ◽  
Fracturing Process

Abstract Unconventional resources have made a significant contribution to fossil energy supply to date, and some specific stimulation techniques have been used in their exploitation. For example, the use of scCO2 foam as a hydraulic fracturing stimulation fluid has sparked considerable interest due to its numerous advantages in terms of fracturing and production performance. The strength of scCO2 foam, an indicator of foam performance, highly depends on the formulation design, foaming properties and operating conditions. Due to complex nature of foam, the quantification of foam strength at downhole conditions is challenging. Specific screening and optimization processes are required to design high performance foam. Although the flow behavior (apparent viscosity) of foam has been extensively studied with empirical models, integrating some essential process parameters into the foam flow behavior evaluation remains challenging. In this study, we present an effective model that incorporates the benefits of a deep learning (DL) approach while taking into account the integration of specific process variables. Several input parameters such as surfactant types and concentration, salinity, polymer concentration, temperature and pressure were used in conjunction with foam quality and shear rate. To predict foam strength while taking the aforementioned parameters into account, a deep neural network (DNN) with optimized hyperparameters was developed. The experimental data for this purpose were obtained using a pressurized foam rheometer. An improved deep learning framework was developed and designed to learn the intrinsic relation among various parameters. The predictive study concludes that, the developed optimized DNN algorithm can provide a reliable and robust prediction with significantly high accuracy. When compared to a shallow network with a standard deviation of less than 5%, the developed optimal deep neural network increased average predictive accuracy to 95.64%. The regression coefficient in the optimized case was found to be nearly one with a low mean square error. The developed DNN algorithm is considered as an improved framework which encompasses several process variables and provides reliable and accurate prediction thus makes it suitable for further integration with fracturing simulator. It would also be helpful for optimizing fracturing process and improving foam formulations.

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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