An automated data-driven pressure transient analysis of water-drive gas reservoir through the coupled machine learning and ensemble Kalman filter method

2021 ◽  
pp. 109492
Author(s):  
Liang Xue ◽  
Shaohua Gu ◽  
Lidong Mi ◽  
Lin Zhao ◽  
Yuetian Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Transient Analysis ◽  
Data Driven ◽  
Filter Method ◽  
Gas Reservoir ◽  
Pressure Transient Analysis ◽  
Pressure Transient ◽  
Kalman Filter Method

Machine learning based conditional mean filter: a non-linear extension of the ensemble Kalman filter 

2021 ◽  
Author(s):  
Truong-Vinh Hoang ◽  
Sebastian Krumscheid ◽  
Raul Tempone
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Machine Learning Techniques ◽  
Filter Method ◽  
Gaussian State ◽  
Correct Prediction ◽  
Conditional Mean ◽  
Non Linear ◽  
Mean Filter

<p>Filtering is an uncertainty quantification technique that refers to the inference of the states of dynamical systems from noisy observations. This work proposes a machine learning-based filtering method for tracking the high-dimensional non-Gaussian state-space models with non-linear dynamics and sparse observations. Our filter method is based on the conditional expectation mean filter and uses machine-learning techniques to approximate the conditional mean (CM). The contribution of this work is twofolds: (i) we demonstrate theoretically that the assimilated ensembles obtained using the ensemble conditional mean filter (EnCMF) provide a correct prediction of the posterior mean and have the optimal variance, and (ii) we implement the EnCMF using artificial neural networks, which has a significant advantage in representing non-linear functions that map between high-dimensionality domains, such as the CM. We implement the machine learning-based EnCMF for tracking the states of the Lorenz-63 and 96 systems under the chaotic regime. Numerical results show that the EnCMF outperforms the ensemble Kalman filter.</p>

scholarly journals Solution Estimation of Logistic Growth Model with Ensemble Kalman Filter Method

2014 ◽  
Vol 14 (2) ◽  
pp. 85
Author(s):  
Vianda Nuning Fitriani ◽  
Kosala Dwidja Purnomo
Keyword(s):  
Kalman Filter ◽  
Analytical Solution ◽  
Ensemble Kalman Filter ◽  
Nonlinear Models ◽  
Specific Form ◽  
Filter Method ◽  
Logistic Growth ◽  
Parabolic Form ◽  
Logistic Growth Model ◽  
Kalman Filter Method

Ensemble Kalman Filter (EnKF) can be applied for linear or nonlinear models. This paper is aimed to estimate the logistic growth of population models using EnKF. The estimation will be compared with the analytical solution. We assume that we can find the analytical solution of the models. The models is in the specific form i.e comparison between the population growth rate and the amount of population is in the parabolic form. The good estimation will be attained by choosing 100 as size of ensembles in EnKF. The result of estimation really so closed to the analytical solution. Keywords : Analytical solution, EnKF, ensemble

Prediction of Perforation Efficiency in Gas Well Using Machine Learning

2021 ◽  
Author(s):  
Sukotrihadiyono Tejo ◽  
Yasutra Amega ◽  
Irawan Dedy
Keyword(s):  
Machine Learning ◽  
Test Data ◽  
Transient Analysis ◽  
Empirical Relation ◽  
Skin Damage ◽  
Gas Well ◽  
Pressure Transient Analysis ◽  
Pressure Transient ◽  
Rate Test ◽  
Short Time

Abstract The efficiency of perforation is an important aspect in gas well since it affects near wellbore pressure drop related to turbulent flow. The perforation efficiency is correlated with non-Darcy skin that is able to be distinguished by pressure transient analysis of isochronal test (Swift et al., 1962), or evaluated from multi-rate flow test data plot coefficients (Jones et al., 1976), or type curve of single build up test following constant-rate production (Spivey et al., 2004). A simple single rate pressure transient analysis which is supported by parameters derived from historical multi rate test data was also proven to differentiate skin damage and non-Darcy skin (Aminian et al., 2007). Unfortunately there are trade-offs between accurateness and analysis time in these aforementioned methods. Quick analysis of perforation efficiency is often needed during well completion and workover activities, to decide whether re-perforation job is required or not. To overcome the challenges of limited time for data acquisition and evaluation, an empirical relation between actual perforation length, skin damage, and laminar-turbulence flow coefficients that are obtained from short-time multi rate test is important to predict the perforation efficiency. The empirical relation will be developed using machine learning. A simple gas reservoir model is built and then run with variations of reservoir permeability, perforation interval length, near wellbore permeability, and vertical anisotropy to generate large numbers of hypothetical multi rate test data. The data set of laminar coefficient, turbulence coefficient, absolute open flow, skin damage, and perforation length will then be trained and tested to create empirical relation using supervised regression method which will afterwards be applied to several actual field cases. This study will elaborate the development of empirical relation of perforation efficiency with the distinct parameters obtained from simple short-time multi rate test data, what other factors will influence the empirical relation, as well as become the possible condition limit of the field application of the developed empirical relation.

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