Combining Capacitance Resistance Model with Geological Data for Large Reservoirs

2021 ◽  
Author(s):  
Davud Davudov ◽  
Ademide O. Mabadeje ◽  
Anton Malkov ◽  
Ashwin Venkatraman ◽  
Birol Dindoruk
Keyword(s):  
Hybrid Model ◽  
Field Data ◽  
Water Injection ◽  
Hybrid Approach ◽  
Synthetic Data ◽  
Operating Conditions ◽  
Order Estimate ◽  
Geological Data ◽  
Fast Marching ◽  
Computational Speed

Abstract Capacitance resistance models (CRM) constitute reduced physics-based models that provide quantitative first order estimate of inter-well connectivity using production and injection data (without geology). However, application of these models to mature fields with large number of wells along with historical data and varying operating conditions is challenging and computationally expensive. In this study we present a novel hybrid approach that combines CRM with geological data for application to large reservoirs. CRM models become more challenging, when applied in larger domains, because the number of unknowns they assume for every injector and producer pair are inherently connected. To overcome this complication, we obtain radius of investigation (ROI) for every injector using Fast Marching Model (FMM) that characterizes the field based on the geological data. Specifically, FMM is used to determine unrelated producer-injector pairs and ROI for each injector that reduce the number of unknowns and hence computational complexity. This hybrid model approach is validated by comparing results with a standalone CRM model for both synthetic and field data. We validate the proposed method results using synthetic data for a producing field with 5 producers and 4 injectors that is generated by numerical simulation. The FMM accurately identifies unrelated injection-production pairs to reduce the number of unknowns in CRM model by 35% (20 to 13). After conceptual validation, we apply this hybrid approach to a mature field with 29 injectors and 46 producers where the number of unknowns are reduced by 57% after the non-related pairs are determined with FMM. This results in significant speedup of computations as compared to standalone CRM approach. In totality, we have developed and validated the proposed hybrid model of geology and CRM using synthetic and field data. On applying our learnings successfully, we propose a hybrid model that combines two reduced-physics models (CRM and FMM) to decrease computational speed and reduce uncertainty in the field. With increasing focus on digitization, these workflows can help organizations reallocate water injection without CAPEX to deliver return on investment for digitization projects.

scholarly journals New Hybrid Approach for Developing Automated Machine Learning Workflows: A Real Case Application in Evaluation of Marcellus Shale Gas Production

Fuels ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 286-303
Author(s):  
Vuong Van Pham ◽  
Ebrahim Fathi ◽  
Fatemeh Belyadi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Shale Gas ◽  
Field Data ◽  
Marcellus Shale ◽  
Hybrid Approach ◽  
Gas Production ◽  
Bayesian Optimization ◽  
Real Field ◽  
Engineering Problems

The success of machine learning (ML) techniques implemented in different industries heavily rely on operator expertise and domain knowledge, which is used in manually choosing an algorithm and setting up the specific algorithm parameters for a problem. Due to the manual nature of model selection and parameter tuning, it is impossible to quantify or evaluate the quality of this manual process, which in turn limits the ability to perform comparison studies between different algorithms. In this study, we propose a new hybrid approach for developing machine learning workflows to help automated algorithm selection and hyperparameter optimization. The proposed approach provides a robust, reproducible, and unbiased workflow that can be quantified and validated using different scoring metrics. We have used the most common workflows implemented in the application of artificial intelligence (AI) and ML in engineering problems including grid/random search, Bayesian search and optimization, genetic programming, and compared that with our new hybrid approach that includes the integration of Tree-based Pipeline Optimization Tool (TPOT) and Bayesian optimization. The performance of each workflow is quantified using different scoring metrics such as Pearson correlation (i.e., R2 correlation) and Mean Square Error (i.e., MSE). For this purpose, actual field data obtained from 1567 gas wells in Marcellus Shale, with 121 features from reservoir, drilling, completion, stimulation, and operation is tested using different proposed workflows. A proposed new hybrid workflow is then used to evaluate the type well used for evaluation of Marcellus shale gas production. In conclusion, our automated hybrid approach showed significant improvement in comparison to other proposed workflows using both scoring matrices. The new hybrid approach provides a practical tool that supports the automated model and hyperparameter selection, which is tested using real field data that can be implemented in solving different engineering problems using artificial intelligence and machine learning. The new hybrid model is tested in a real field and compared with conventional type wells developed by field engineers. It is found that the type well of the field is very close to P50 predictions of the field, which shows great success in the completion design of the field performed by field engineers. It also shows that the field average production could have been improved by 8% if shorter cluster spacing and higher proppant loading per cluster were used during the frac jobs.

Hybrid approach combining EMD, ARIMA and monte carlo for multi-step ahead medical tourism forecasting

2021 ◽  
pp. 1-17
Author(s):  
Nuzhat Fatema ◽  
H Malik ◽  
Mutia Sobihah Binti Abd Halim
Keyword(s):  
Monte Carlo ◽  
Hybrid Model ◽  
Medical Tourism ◽  
Moving Average ◽  
Hybrid Approach ◽  
Mode Decomposition ◽  
Hybrid Forecasting ◽  
Input Variables ◽  
Intelligent Approach

This paper proposed a hybrid intelligent approach based on empirical mode decomposition (EMD), autoregressive integrated moving average (ARIMA) and Monte Carlo simulation (MCS) methods for multi-step ahead medical tourism (MT) forecasting using explanatory input variables based on two decade real-time recorded database. In the proposed hybrid model, these variables are 1st extracted then medical tourism is forecasted to perform the long term as well as the short term goal and planning in the nation. The multi-step ahead medical tourism is forecasted recursively, by utilizing the 1st forecasted value as the input variable to generate the next forecasting value and this procedure is continued till third step ahead forecasted value. The proposed approach firstly tested and validated by using international tourism arrival (ITA) dataset then proposed approach is implemented for forecasting of medical tourism arrival in nation. In order to validate the performance and accuracy of the proposed hybrid model, a comparative analysis is performed by using Monte Carlo method and the results are compared. Obtained results shows that the proposed hybrid forecasting approach for medical tourism has outperformance characteristics.

scholarly journals Bringing Kano’s Perspective to AHP: The 2D-AHP Decision Model

2016 ◽  
Vol 7 (4) ◽  
pp. 16-26
Author(s):  
Uk Jung ◽  
Seongmin Yim ◽  
Sunguk Lim ◽  
Chongman Kim
Keyword(s):  
Hybrid Model ◽  
Decision Model ◽  
Hybrid Approach ◽  
Survey Study ◽  
Test Case ◽  
Problem Structure ◽  
Kano Model ◽  
Two Dimension ◽  
True Hybrid ◽  
Decision Making Model

AbstractAHP and the Kano model are such prevalent TQM tools that it may be surprising that a true hybrid decision-making model has so far eluded researchers. The quest for a hybrid approach is complicated by the differing output perspective of each model, namely discrete ranking (AHP) versus a multi-dimensional picture (Kano). This paper presents a hybrid model of AHP and Kano model, so called two-dimension AHP (2D-AHP).This paper first compares the two approaches and justifies a hybrid model based on a simple conceit drawn from the Kano perspective: given a decision hierarchy, child and parent elements can exhibit multi-dimension relationships under different circumstances. Based on this premise, the authors construct a hybrid two-dimension AHP model whereby a functional-dysfunctional question-pair technique is incorporated into a traditional AHP framework.Using the proposed hybrid model, this paper provides a practical test case of its implementation. The 2D-AHP approach revealed important evaluation variances obscured through AHP, while a survey study confirmed that the 2D-AHP approach is both feasible and preferred in some respects by respondents.Although there have been rich research efforts to combine AHP and Kano model, most of them is simply about a series of individual usage of each methodology. On the other hand, the type of hybridization between AHP and Kano model in this paper is quite unique in terms of the two dimensional perspective. The model provides a general approach with application possibilities far beyond the scope of the test case and its problem structure, and so calls for application and validation in new cases.

scholarly journals Coupled hydrogeophysical parameter estimation using a sequential Bayesian approach

2010 ◽  
Vol 14 (3) ◽  
pp. 545-556 ◽  
Author(s):  
J. Rings ◽  
J. A. Huisman ◽  
H. Vereecken
Keyword(s):  
Water Content ◽  
Particle Filtering ◽  
Field Data ◽  
Synthetic Data ◽  
Time Domain Reflectometry ◽  
Second Step ◽  
Parameter Estimates ◽  
Posterior Distributions ◽  
Data Set ◽  
Resistivity Tomography

Abstract. Coupled hydrogeophysical methods infer hydrological and petrophysical parameters directly from geophysical measurements. Widespread methods do not explicitly recognize uncertainty in parameter estimates. Therefore, we apply a sequential Bayesian framework that provides updates of state, parameters and their uncertainty whenever measurements become available. We have coupled a hydrological and an electrical resistivity tomography (ERT) forward code in a particle filtering framework. First, we analyze a synthetic data set of lysimeter infiltration monitored with ERT. In a second step, we apply the approach to field data measured during an infiltration event on a full-scale dike model. For the synthetic data, the water content distribution and the hydraulic conductivity are accurately estimated after a few time steps. For the field data, hydraulic parameters are successfully estimated from water content measurements made with spatial time domain reflectometry and ERT, and the development of their posterior distributions is shown.

Combining Physics-Based and Data-Driven Models for Estimation of WOB During Ultra-Deep Ocean Drilling

2018 ◽  
Author(s):  
Tatsuya Kaneko ◽  
Ryota Wada ◽  
Masahiko Ozaki ◽  
Tomoya Inoue
Keyword(s):  
Hybrid Model ◽  
Numerical Models ◽  
Drill String ◽  
Operating Conditions ◽  
Data Driven ◽  
High Frequency Oscillation ◽  
Lumped Mass ◽  
Unknown Parameters ◽  
Mass Model ◽  
Time Operation

Offshore drilling with drill string over 10,000m long has many technical challenges. Among them, the challenge to control the weight on bit (WOB) between a certain range is inevitable for the integrity of drill pipes and the efficiency of the drilling operation. Since WOB cannot be monitored directly during drilling, the tension at the top of the drill string is used as an indicator of the WOB. However, WOB and the surface measured tension are known to show different features. The deviation among the two is due to the dynamic longitudinal behavior of the drill string, which becomes stronger as the drill string gets longer and more elastic. One feature of the difference is related to the occurrence of high-frequency oscillation. We have analyzed the longitudinal behavior of drill string with lumped-mass model and captured the descriptive behavior of such phenomena. However, such physics-based models are not sufficient for real-time operation. There are many unknown parameters that need to be tuned to fit the actual operating conditions. In addition, the huge and complex drilling system will have non-linear behavior, especially near the drilling annulus. These features will only be captured in the data obtained during operation. The proposed hybrid model is a combination of physics-based models and data-driven models. The basic idea is to utilize data-driven techniques to integrate the obtained data during operation into the physics-based model. There are many options on how far we integrate the data-driven techniques to the physics-based model. For example, we have been successful in estimating the WOB from the surface measured tension and the displacement of the drill string top with only recurrent neural networks (RNNs), provided we have enough data of WOB. Lack of WOB measurement cannot be avoided, so the amount of data needs to be increased by utilizing results from physics-based numerical models. The aim of the research is to find a good combination of the two models. In this paper, we will discuss several hybrid model configurations and its performance.

scholarly journals Predictive Modelling of Wellhead Corrosion due to Operating Conditions: A Field Data Approach

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Chinedu I. Ossai
Keyword(s):  
Corrosion Rate ◽  
Field Data ◽  
Oil And Gas ◽  
Multiple Linear Regression Model ◽  
Predictive Modelling ◽  
Operating Conditions ◽  
Experimental Results ◽  
Operating Parameters ◽  
Oil And Gas Fields ◽  
The Impact

The flow of crude oil, water, and gas from the reservoirs through the wellheads results in its deterioration. This deterioration which is due to the impact of turbulence, corrosion, and erosion significantly reduces the integrity of the wellheads. Effectively managing the wellheads, therefore, requires the knowledge of the extent to which these factors contribute to its degradation. In this paper, the contribution of some operating parameters (temperature, CO2 partial pressure, flow rate, and pH) on the corrosion rate of oil and gas wellheads was studied. Field data from onshore oil and gas fields were analysed with multiple linear regression model to determine the dependency of the corrosion rate on the operating parameters. ANOVA, value test, and multiple regression coefficients were used in the statistical analysis of the results, while in previous experimental results, de Waard-Milliams models and de Waard-Lotz model were used to validate the modelled wellhead corrosion rates. The study shows that the operating parameters contribute to about 26% of the wellhead corrosion rate. The predicted corrosion models also showed a good agreement with the field data and the de Waard-Lotz models but mixed results with the experimental results and the de Waard-Milliams models.

scholarly journals Manipulation of the swirling flow instability in hydraulic turbine diffuser by different methods of water injection

2018 ◽  
Vol 180 ◽  
pp. 02090 ◽  
Author(s):  
Pavel Rudolf ◽  
Jiří Litera ◽  
Germán Alejandro Ibarra Bolanos ◽  
David Štefan
Keyword(s):  
Swirling Flow ◽  
Flow Instability ◽  
Draft Tube ◽  
Water Injection ◽  
Hydraulic Turbine ◽  
Operating Conditions ◽  
Francis Turbine ◽  
Necessary Condition ◽  
Vortex Rope ◽  
Wide Range

Vortex rope, which induces substantial pressure pulsations, arises in the draft tube (diffuser) of Francis turbine for off-design operating conditions. Present paper focuses on mitigation of those pulsations using active water jet injection control. Several modifications of the original Susan-Resiga’s idea were proposed. All modifications are driven by manipulation of the shear layer region, which is believed to play important role in swirling flow instability. While some of the methods provide results close to the original one, none of them works in such a wide range. Series of numerical experiments support the idea that the necessary condition for vortex rope pulsation mitigation is increasing the fluid momentum along the draft tube axis.

scholarly journals Combining chronicle mining and semantics for predictive maintenance in manufacturing processes

Semantic Web ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 927-948 ◽  
Author(s):  
Qiushi Cao ◽  
Ahmed Samet ◽  
Cecilia Zanni-Merk ◽  
François de Bertrand de Beuvron ◽  
Christoph Reich
Keyword(s):  
Data Mining ◽  
Hybrid Approach ◽  
Failure Prediction ◽  
Operating Conditions ◽  
Predictive Maintenance ◽  
Semantic Gap ◽  
Manufacturing Processes ◽  
Temporal Constraints ◽  
Industrial Data ◽  
The Future

Within manufacturing processes, faults and failures may cause severe economic loss. With the vision of Industry 4.0, artificial intelligence techniques such as data mining play a crucial role in automatic fault and failure prediction. However, due to the heterogeneous nature of industrial data, data mining results normally lack both machine and human-understandable representation and interpretation of knowledge. This may cause the semantic gap issue, which stands for the incoherence between the knowledge extracted from industrial data and the interpretation of the knowledge from a user. To address this issue, ontology-based approaches have been used to bridge the semantic gap between data mining results and users. However, only a few existing ontology-based approaches provide satisfactory knowledge modeling and representation for all the essential concepts in predictive maintenance. Moreover, most of the existing research works merely focus on the classification of operating conditions of machines, while lacking the extraction of specific temporal information of failure occurrence. This brings obstacles for users to perform maintenance actions with the consideration of temporal constraints. To tackle these challenges, in this paper we introduce a novel hybrid approach to facilitate predictive maintenance tasks in manufacturing processes. The proposed approach is a combination of data mining and semantics, within which chronicle mining is used to predict the future failures of the monitored industrial machinery, and a Manufacturing Predictive Maintenance Ontology (MPMO) with its rule-based extension is used to predict temporal constraints of failures and to represent the predictive results formally. As a result, Semantic Web Rule Language (SWRL) rules are constructed for predicting the occurrence time of machinery failures in the future. The proposed rules provide explicit knowledge representation and semantic enrichment of failure prediction results, thus easing the understanding of the inferred knowledge. A case study on a semi-conductor manufacturing process is used to demonstrate our approach in detail. The evaluation of results shows that the MPMO ontology is free of bad practices in the structural, functional, and usability-profiling dimensions. The constructed SWRL rules posses more than 80% of True Positive Rate, Precision, and F-measure, which shows promising performance in failure prediction.

A new method for absolute underground positioning based on transient electromagnetics

2019 ◽  
Vol 221 (1) ◽  
pp. 87-96
Author(s):  
S Malecki ◽  
R-U Börner ◽  
K Spitzer
Keyword(s):  
Field Data ◽  
Synthetic Data ◽  
Trust Region ◽  
Trust Region Algorithm ◽  
Location Data ◽  
The Earth ◽  
Time Regime ◽  
Em Method ◽  
Em Field ◽  
Confidence Ellipses

SUMMARY We present a procedure for localizing underground positions using a time-domain inductive electromagnetic (EM) method. The position to be localized is associated with an EM receiver placed inside the Earth. An EM field is generated by one or more transmitters located at known positions at the Earth’s surface. We then invert the EM field data for the receiver positions using a trust-region algorithm. For any given time regime and source–receiver geometry, the propagation of the electromagnetic fields is determined by the electrical conductivity distribution within the Earth. We show that it is sufficient to use a simple 1-D model to recover the receiver positions with reasonable accuracy. Generally, we demonstrate the robustness of the presented approach. Using confidence ellipses and confidence intervals we assess the accuracy of the recovered location data. The proposed method has been extensively tested against synthetic data obtained by numerical experiments. Furthermore, we have successfully carried out a location recovery using field data. The field data were recorded within a borehole in Alberta (Canada) at 101.4 m depth. The recovered location of the borehole receiver differs from the actual location by 0.70 m in the horizontal plane and by 0.82 m in depth.

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