scholarly journals A Review of Groundwater Management Models with a Focus on IoT-Based Systems

2021 ◽  
Vol 14 (1) ◽  
pp. 148
Author(s):  
Banjo Ayoade Aderemi ◽  
Thomas Otieno Olwal ◽  
Julius Musyoka Ndambuki ◽  
Sophia Sudi Rwanga
Keyword(s):  
Groundwater Management ◽  
Distributed Storage ◽  
Groundwater Resources ◽  
Data Gathering ◽  
Land Usage ◽  
Management Models ◽  
Management Objectives ◽  
Efficient Data ◽  
The Moment ◽  
The Internet Of Things

Globally, groundwater is the largest distributed storage of freshwater and plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, groundwater resources are dynamic and often change as a result of land usage, abstraction, as well as variation in climate. To solve these challenges, many conventional solutions, such as certain numerical techniques, have been proffered for groundwater modelling. The global evolution of the Internet of Things (IoT) has enhanced the culture of data gathering for the management of groundwater resources. In addition, efficient data-driven groundwater resource management relies hugely on information relating to changes in groundwater resources as well as their availability. At the moment, some studies in the literature reveal that groundwater managers lack an efficient and real-time groundwater management system which is needed to gather the required data. Additionally, the literature reveals that the existing methods of collecting data lack the required efficiency to meet computational model requirements and meet management objectives. Unlike previous surveys, which solely focussed on particular groundwater issues related to simulation and optimisation management methods, this paper seeks to highlight the current groundwater management models as well as the IoT contributions.

scholarly journals A Review of Groundwater Management Models with a Focus on IoT-Based Systems

2021 ◽  
Author(s):  
Banjo Aderemi ◽  
Thomas Otieno Olwal ◽  
Julius Musyoka Ndambuki ◽  
Sophia Sudi Rwanga
Keyword(s):  
Groundwater Management ◽  
Distributed Storage ◽  
Groundwater Resources ◽  
Data Gathering ◽  
Land Usage ◽  
Management Models ◽  
Management Objectives ◽  
Efficient Data ◽  
The Moment ◽  
The Internet Of Things

Globally, groundwater is the largest distributed storage of freshwater and plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, groundwater resources are dynamic and often change as a result of land usage, abstraction, as well as variation in climate. To solve these challenges, many conventional solutions, such as certain numerical techniques, have been proffered for groundwater modelling. The global evolution of the Internet of Things (IoT) has enhanced the culture of data gathering for the management of groundwater resources. In addition, efficient data-driven groundwater resource management relies hugely on information relating to changes in groundwater resources as well as their availability. At the moment, some studies in the literature reveal that groundwater managers lack an efficient and real-time groundwater management system that is needed to gather the required data. Additionally, the literature reveals that the existing methods of collecting data lack the required efficiency to meet computational model requirements and meet management objectives. Unlike previous surveys, which solely focussed on particular groundwater issues related to simulation and optimisation management methods, this paper seeks to highlight the current groundwater management models as well as the IoT contributions.

scholarly journals Groundwater Level Resources Management Modelling: A Review

2021 ◽  
Author(s):  
Banjo Ayoade Aderemi ◽  
Thomas Otieno Olwal ◽  
Julius Musyoka Ndambuki ◽  
Sophiar S. Rwanga
Keyword(s):  
Resource Management ◽  
Groundwater Level ◽  
Distributed Storage ◽  
Groundwater Resources ◽  
Data Gathering ◽  
Data Driven ◽  
The Internet ◽  
Resources Management ◽  
Management Models ◽  
Efficient Data

Globally, groundwater is the largest distributed storage of freshwater that plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, this resource is not unlimited and its sustainability is highly dependent on its prudent use. Thus, efficient management of groundwater resources to prevent overexploitation, scarcity and drought has become a major challenge for researchers as well as water managers. To solve these challenges, many solutions such as simulation and optimisation models have been proffered through the use of historical data. Therefore, this has made efficient data gathering essential to maintain data-driven groundwater level resource management models from the observation site. The global evolution of the Internet of Things (IoTs), has increased the nature of data gathering for the management of groundwater resources. Recently, a number of research challenges such as the lack of computational efficiency and scalability due to uncertainties from input parameters to the groundwater level resource model have been revealed in the management of groundwater level resources. In addition, efficient data-driven groundwater level resource management relies hugely on information relating to changes in groundwater resource levels as well as its availability. At the moment, the groundwater managers lack an efficient and scalable groundwater management system to gather the required data. The literature revealed that the existing methods of collecting data lack efficiency to meet computational model requirements and meet management objectives. Although the IoTs enabled automated data processing systems are in existence by transmitting the generated data from IoT enabled devices into the cloud through the Internet. However, traditional IoT Internet is not scalable and efficient enough to process the generated vast IoT data. Thus, it is necessary to have an efficient and scalable IoT system to extract valuable information in real-time for groundwater level resource management. Unlike previous surveys which solely focussed on particular groundwater issues related to simulation and optimisation management models, nonetheless, this paper seeks to highlight the current groundwater level resources management models as well as the IoT contributions.

scholarly journals Groundwater Resources Management Modelling: A Review

2021 ◽  
Author(s):  
Banjo Ayoade Aderemi ◽  
Thomas Otieno Olwal ◽  
Julius Musyoka Ndambuki ◽  
Sophiar S Rwanga
Keyword(s):  
Resource Management ◽  
Groundwater Management ◽  
Groundwater Resources ◽  
Data Gathering ◽  
Groundwater Resource ◽  
Data Driven ◽  
The Internet ◽  
Resources Management ◽  
Efficient Data ◽  
Groundwater Resources Management

Globally, groundwater is the largest distributed storage of freshwater that plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, groundwater resources are dynamic and often changes as a result of land usage, abstraction as well as variation in climate. Thus, efficient management of groundwater resources to prevent overexploitation, scarcity, and minimising the effects of drought has become a major challenge for researchers as well as water managers. Furthermore, a number of research challenges such as the lack of computational efficiency and scalability due to uncertainties from input parameters to the groundwater resource model have been revealed in the management of groundwater resources. To solve these challenges, many conventional solutions such as numerical techniques have been proffered for groundwater modelling. Also, the use of data-driven techniques such as machine learning is gaining more attraction to solve these aforementioned challenges. Thus, this has made efficient data gathering essential to maintain da-ta-driven groundwater resources management models from the observation site. The global evolution of the Internet of Things (IoTs), has increased the nature of data gathering for the management of groundwater resources. In addition, efficient data-driven groundwater resource management relies hugely on information relating to changes in groundwater resources as well as their availability. Although the IoTs enabled automated data processing systems are in existence by transmitting the generated data from IoT enabled devices into the cloud through the Internet. However, traditional IoT Internet is not scalable and efficient enough to process the generated vast IoT data At the moment, some pieces of the literature revealed the groundwater managers lack an efficient, scalable and real-time groundwater management system to gather the required data. Also, the literature revealed that the existing methods of collecting data lack efficiency to meet computational model requirements and meet management objectives. Thus, it is necessary to have an efficient and scalable IoT system to extract valuable information in real-time for groundwater resource management. Unlike previous surveys which solely focussed on particular groundwater issues related to simulation and optimisation management methods, nonetheless, this paper seeks to highlight the current groundwater management models as well as the IoT contributions

Mahakam Field Characterization Using Production Type-Curve With Business Intelligence Application

2020 ◽  
Author(s):  
P. Noverri
Keyword(s):  
Business Intelligence ◽  
Data Gathering ◽  
Production Data ◽  
Type Curve ◽  
Type Curves ◽  
Production Type ◽  
Efficient Data ◽  
Production Behavior ◽  
Factor Type ◽  
Mahakam Delta

Delta Mahakam is a giant hydrocarbon block which is comprised two oil fields and five gas fields. The giant block has been considered mature after production for more than 40 years. More than 2,000 wells have been drilled to optimize hydrocarbon recovery. From those wells, a huge amount of production data is available and documented in a well-structured manner. Gaining insight from this data is highly beneficial to understand fields behavior and their characteristics. The fields production characterization is analyzed with Production Type-Curve method. In this case, type curves were generated from production data ratio such as CGR, WGR and GOR to field recovery factor. Type curve is considered as a simple approach to find patterns and capture a helicopter view from a huge volume of production data. Utilization of business intelligence enables efficient data gathering from different data sources, data preparation and data visualization through dashboards. Each dashboard provides a different perspective which consists of field view, zone view, sector view and POD view. Dashboards allow users to perform comprehensive analysis in describing production behavior. Production type-curve analysis through dashboards show that fields in the Mahakam Delta can be grouped based on their production behavior and effectively provide global field understanding Discovery of production key information from proposed methods can be used as reference for prospective and existing fields development in the Mahakam Delta. This paper demonstrates an example of production type-curve as a simple yet efficient method in characterizing field production behaviors which is realized by a Business Intelligent application

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