An Innovative Artificial Intelligence Framework for Reducing Carbon Footprint in Reservoir Management

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
Abdulkarim Al Sofi ◽  
Alberto Marsala ◽  
Ali Yousif
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Carbon Footprint ◽  
Carbon Emissions ◽  
Water Injection ◽  
Reservoir Management ◽  
Development Plan ◽  
Production Rates ◽  
Monitoring Method ◽  
The Impact

Abstract The energy industry has been transformed considerably in the last years. Sustainable development of oil and gas reservoir has become a major driver for these energy companies, and strengthened the focus to maximize hydrocarbon extraction while minimizing the associated carbon footprint. The focus has been further on maximizing efficiency and waste reduction in order to enhance profitability of projects. Challenges still remain in terms of that the carbon emissions from oilfield operations, related to the production, disposal and utilization of water and hydrocarbons, may be significant and the objective of increasing production has to be traded off in many instances against the quest for reducing carbon emissions. The fourth industrial revolution has brought new opportunities for companies to enhance decision making in their upstream development and optimize their recovery potential while minimizing the carbon footprint and associated cost. In this work, we present a smart approach for optimizing recovery while minimizing the carbon footprint of a reservoir in terms of the associated development and production activities. We use an advanced nonlinear autoregressive neural network approach integrated with time-lapse electromagnetic monitoring data to forecast production and carbon emissions from the reservoir in real-time, under uncertainty. The artificial intelligence approach also allows to investigate a circular carbon approach, where the produced greenhouse gases are re-injected into the well, while at the same time adjusting water injection levels. This allows to forecast and analyze the impact of a circular development plan. We tested the AI framework on a synthetic reservoir encompassing a complex carbonate fracture system and well setup. The carbon emissions were forecasted in real-time based on the previous production rates and the defined injection levels. The forecasted carbon emissions were then integrated into an optimization technique, in order to adjust injection levels to minimize water cut and overall carbon emissions, while optimizing production rates. Results were promising and highlighted the potential significant reductions in carbon emissions for the studied synthetic reservoir case. Moreover, the deployment of deep electromagnetic surveys was proved particularly beneficial as a deep formation evaluation monitoring method for tracking the injected waterfront inside the reservoir and optimizing the sweep efficiency, while minimizing the inefficient use of water injection. Accordingly, such integrated AI approach has a twofold benefit: maximizing the hydrocarbon productivity, while minimizing the water consumption and associated carbon emissions. Such framework represents a paradigm shift in reservoir management and improved oil recovery operations under uncertainty. It proposes an innovative integrated methodology to reduce the carbon footprint and attain a real-time efficient circular development plan.

scholarly journals The Impact of Climate Change on the Value of Growing Maize as a Biofuel

2020 ◽  
Vol 4 (1) ◽  
pp. 13-26
Author(s):  
Sally Olasogba ◽  
Les DUCKERS
Keyword(s):  
Climate Change ◽  
Carbon Footprint ◽  
Carbon Emissions ◽  
Energy Use ◽  
Farm Management ◽  
Energy Output ◽  
Net Energy ◽  
Primary Concern ◽  
Impact Of Climate Change ◽  
The Impact

Abstract: Aim: According to COP23, Climate Change threatens the stability of the planet’s ecosystems, with a tipping point believed to be at only +2°C.  With the burning of fossil fuels, held responsible for the release of much of the greenhouse gases, a sensible world- wide strategy is to replace fossil fuel energy sources with renewable ones. The renewable resources such as wind, hydro, geothermal, wave and tidal energies are found in particular geographical locations whereas almost every country is potentially able to exploit PV and biomass. This paper examines the role that changing climate could have on the growing and processing of biomass. The primary concern is that future climates could adversely affect the yield of crops, and hence the potential contribution of biomass to the strategy to combat climate change. Maize, a C4 crop, was selected for the study because it can be processed into biogas or other biofuels. Four different Nigerian agricultural zones growing maize were chosen for the study. Long-term weather data was available for the four sites and this permitted the modelling of future climates. Design / Research methods: The results of this study come from modelling future climates and applying this to crop models. This unique work, which has integrated climate change and crop modelling to forecast yield and carbon emissions, reveals how maize responds to the predicted increased temperature, change in rainfall, and the variation in weather patterns. In order to fully assess a biomass crop, the full energy cycle and carbon emissions were estimated based on energy and materials inputs involved in farm management: fertilizer application, and tillage type. For maize to support the replacement strategy mentioned above it is essential that the ratio of energy output to energy input exceeds 1, but of course it should be as large as possible. Conclusions / findings: Results demonstrate that the influence of climate change is important and in many scenarios, acts to reduce yield, but that the negative effects can be partially mitigated by careful selection of farm management practices. Yield and carbon footprint is particularly sensitive to the application rate of fertilizer across all locations whilst climate change is the causal driver for the increase in net energy and carbon footprint at most locations. Nonetheless, in order to ensure a successful strategic move towards a low carbon future, and sustainable implementation of biofuel policies, this study provides valuable information for the Nigerian government and policy makers on potential AEZs to cultivate maize under climate change. Further research on the carbon footprint of alternative bioenergy feedstock to assess their environmental carbon footprint and net energy is strongly suggested. Originality / value of the article: This paper extends the review on the impact of climate change on maize production to include future impacts on net energy use and carbon footprint using a fully integrated assessment framework. Most studies focus only on current farm energy use and historical climate change impact on farm GHG emissions.   

scholarly journals Artificial Intelligence-Enabled Traffic Monitoring System

2020 ◽  
Vol 12 (21) ◽  
pp. 9177
Author(s):  
Vishal Mandal ◽  
Abdul Rashid Mussah ◽  
Peng Jin ◽  
Yaw Adu-Gyamfi
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Real Time ◽  
Monitoring System ◽  
Traffic Management ◽  
Traffic Monitoring ◽  
Deep Convolutional Neural Networks ◽  
Stage Of Development ◽  
Daunting Task ◽  
The Impact

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stage of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.

scholarly journals The impact of artificial intelligence on commercial management

2020 ◽  
Vol 17 (4) ◽  
pp. 441-452
Author(s):  
Renato Costa ◽  
Álvaro Dias ◽  
Leandro Pereira ◽  
José Santos ◽  
André Capelo
Keyword(s):  
Artificial Intelligence ◽  
Data Collection ◽  
Real Time ◽  
Training And Development ◽  
Analytical Tool ◽  
Commercial Activity ◽  
Business Functions ◽  
The Impact ◽  
Depth Interviews ◽  
Shed Light

The essence of this research is to shed light on use and importance of artificial intelligence (AI) in commercial activity. As such, the objective of the present study is to understand the impact of AI tools on the development of business functions and if they can be affirmed as a means of help or as a substitute for these functions. In-depth interviews were conducted with 15 commercial managers from technological SMEs. The results indicate that all the participants use AI systems frequently, that these tools assist in developing of their functions, allowing having more time and better preparing to solve the commercial problems. The findings also indicate that the tools used by commercials are still somewhat limited, and companies should focus on their training and development in AI, as well as the training of their commercials. Furthermore, the results show that firms intend to use the data collection and the analytical tool that enable real-time response and customization according to customer needs.

Research on Modes of Recycling and Remanufacturing with Considering the Carbon Footprint

2014 ◽  
Vol 936 ◽  
pp. 2368-2373
Author(s):  
Yue Jiang
Keyword(s):  
Carbon Footprint ◽  
Carbon Emissions ◽  
Carbon Tax ◽  
New Product ◽  
Recycling Process ◽  
Optimal Price ◽  
Second Stage ◽  
Selling Process ◽  
The Impact ◽  
Environment Awareness

Three modes of recycling and remanufacturing are discussed when the carbon tax is imposed, the impact of carbon emissions on the mode of recycling and remanufacturing is further analyzed. The results show that the optimal price and sales of the new product and remanufactured product in the second stage are relevant with the carbon emissions in their manufacturing and selling process, not relevant with the carbon emissions in their recycling process. OEM will be more inclined to remanufacture themselves with the increasing environment awareness of consumers.

scholarly journals Artificial intelligence prediction of adverse outcomes in emergency department patients with hyperglycemic crises in real-time

2020 ◽  
Author(s):  
Chin-Chuan Hsu ◽  
Yuan Kao ◽  
Chien-Chin Hsu ◽  
Chia-Jung Chen ◽  
Shu-Lien Hsu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Septic Shock ◽  
Logistic Regression ◽  
Prediction Model ◽  
Real Time ◽  
Adverse Outcomes ◽  
Support Vector ◽  
Icu Admission ◽  
All Cause Mortality ◽  
The Impact

Abstract Background Hyperglycemic crises are associated with high morbidity and mortality. Previous studies proposed methods for predicting adverse outcome in hyperglycemic crises, artificial intelligence (AI) has however never been tried. We implemented an AI prediction model integrated with hospital information system (HIS) to clarify this issue. Methods We included 3,715 patients with hyperglycemic crises from emergency departments (ED) between 2009 and 2018. Patients were randomized into a 70%/30% split for AI model training and testing. Twenty-two feature variables from their electronic medical records were collected, and multilayer perceptron (MLP) was used to construct an AI prediction model to predict sepsis or septic shock, intensive care unit (ICU) admission, and all-cause mortality within 1 month. Comparisons of the performance among random forest, logistic regression, support vector machine (SVM), K-nearest neighbor (KNN), Light Gradient Boosting Machine (LightGBM), and MLP algorithms were also done. Results Using the MLP model, the areas under the curves (AUCs) were 0.808 for sepsis or septic shock, 0.688 for ICU admission, and 0.770 for all-cause mortality. MLP had the best performance in predicting sepsis or septic shock and all-cause mortality, compared with logistic regression, SVM, KNN, and LightGBM. Furthermore, we integrated the AI prediction model with the HIS to assist physicians for decision making in real-time. Conclusions A real-time AI prediction model is a promising method to assist physicians in predicting adverse outcomes in ED patients with hyperglycemic crises. Further studies on the impact on clinical practice and patient outcome are warranted.

scholarly journals Power Loss Prediction for Aging Characteristics and Condition Monitoring for Parallel-Connected Power Modules Using Nonlinear Autoregressive Neural Network

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Shengyou Xu ◽  
Xin Yang ◽  
Li Ran ◽  
Minyou Chen ◽  
Wei Lai
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Condition Monitoring ◽  
Power Loss ◽  
Junction Temperature ◽  
Monitoring Method ◽  
Current Sharing ◽  
Power Modules ◽  
The Impact ◽  
Nonlinear Autoregressive

Power modules connected in parallel may have different electrothermal performance variances resulting from aging because of the nonuniform rate of degradation; different electrothermal performance variances mean different current sharing, different junction temperature, and power losses, which will directly influence the overall characteristics of them. Thus, it is essential to monitor the condition and evaluate the degradation grade to improve the reliability of large-scale power modules. In this paper, the impact of thermal resistance difference on current sharing, junction temperature, and power loss of parallel-connected power modules has been discussed and analyzed. Additionally, a methodology is proposed for condition monitoring and evaluation of the power modules without intruding them by recognizing the increase in external power loss due to internal degradation from aging. In this method, power modules are deemed as a whole system considering only external factors associated with them, all important electrical and thermal parameters are classified as the inputs, and power loss is considered as the output. Firstly, power dissipation is predicted by models using NARX (nonlinear autoregressive with exogenous input) neural network. Then, a monitoring method is illustrated based on the prediction model; a reasonable criterion for the error between the normal and the predicted real-time power loss is established. Finally, the real-time condition and the degradation grade of aging can be evaluated so that the operator can take suitable operating measures by means of this approach. Experimental results validated the effectiveness of the proposed methodology.

Some Practical Issues on Real-Time Reservoir Model Updating Using Ensemble Kalman Filter

SPE Journal ◽  
2007 ◽  
Vol 12 (02) ◽  
pp. 156-166 ◽  
Author(s):  
Xian-Huan Wen ◽  
Wen H. Chen
Keyword(s):  
Real Time ◽  
Ensemble Kalman Filter ◽  
Closed Loop ◽  
Model Updating ◽  
Reservoir Management ◽  
Time Interval ◽  
Ensemble Size ◽  
Reservoir Model ◽  
Reservoir Models ◽  
The Impact

Summary The concept of "closed-loop" reservoir management is currently receiving considerable attention in the petroleum industry. A "real-time" or "continuous" reservoir model updating technique is a critical component for the feasible application of any closed-loop, model-based reservoir management process. This technique should be able to rapidly and continuously update reservoir models assimilating the up-to-date observations of production data so that the performance predictions and the associated uncertainty are up-to-date for optimization of future development/operations. The ensemble Kalman filter (EnKF) method has been shown to be quite efficient for this purpose in large-scale nonlinear systems. Previous studies show that a relatively large ensemble size is required for EnKF to reliably assess the uncertainty, and a confirming step is recommended to ensure the consistency of the updated static and dynamic variables with the flow equations. In this paper, we further explore the capability of EnKF, focusing on some practical issues including the correction of the linear and Gaussian assumptions during filter updating with iteration, the reduction of ensemble size with a resampling scheme, and the impact of data assimilation time interval. Results from the example in this paper demonstrate that the proposed iterative EnKF performs better with more accurate predictions and less uncertainty than the traditional noniterative EnKF. The use of iteration reduces the impact of nonlinearity and non-Gaussianity. Results also show that iteration may only be required when predictions are considerably deviated from the observations. The proposed resampling scheme can significantly reduce the ensemble size necessary for reliable assessment of uncertainty with improved accuracy. Finally, we show that the noniterative EnKF is sensitive to the size of time interval between the assimilation steps. Using the proposed iterative EnKF, results are more stable, more accurate reservoir models and predictions can be obtained even when a large time interval is used. This also indicates that iteration within the EnKF updating serves as a process that corrects the stronger nonlinear and non-Gaussian behaviors when larger time interval is used. Introduction Reservoir models have become an important part of day-to-day decision analysis related to management of oil/gas fields. The closed-loop reservoir management concept (Jansen et al. 2005) allows real-time decisions to be made that maximize the production potential of a reservoir. These decisions are based on the most current information available about the reservoir model and the associated uncertainty of the information. One critical requirement in this real-time, model-based reservoir management process is the ability to rapidly estimate the reservoir models and the associated uncertainty reflecting the most current production data in a real-time fashion. Based on a number of studies, the EnKF method was shown to be well-suited for such applications compared to the traditional history-matching (HM) methods (Evensen 1999; Gu and Oliver 2006; Wen and Chen 2006).

Study on Conduction Paths of Carbon Footprint of Energy Sources Industry among Industries in Henan Province

2012 ◽  
Vol 616-618 ◽  
pp. 1185-1189
Author(s):  
Qing Xin Liu
Keyword(s):  
Carbon Footprint ◽  
Carbon Emissions ◽  
Emission Reduction ◽  
Henan Province ◽  
Energy Industry ◽  
Energy Sources ◽  
Input Output ◽  
Conduction Path ◽  
Output Coefficient ◽  
The Impact

According to Input-Output table portraying the conduction path of carbon footprint among industries, and define the impact of the energy sources industry for other industries on carbon emissions, the paper provide specific and workable methods for energy saving and emission reduction. Constructing the input-output model of carbon footprint of Henan Province by Input-Output method based on the energy consumption data. Using input coefficient, output coefficient and the method of triangulation, identify conduction path of carbon footprint of the energy sources industry among industries in Henan Province. According to the analysis of energy industry in Henan province, the paper introduces the characteristic of carbon emissions of the energy industry and Put forward the ideas of energy conservation and emission reduction taking the energy industry as the breakthrough point.

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