Using Marketing Technology in Reservoir Engineering: The Application of Data Driven Predictive Analysis on a Mature Oil Field

2011 ◽  
Author(s):  
Ellen Bertina Zijlstra ◽  
Gael Riethmuller ◽  
Susanne Schaeftlein ◽  
Salima Saif Al Mahruqi ◽  
Ali Naamani ◽  
...  
Keyword(s):  
Oil Field ◽  
Data Driven ◽  
Reservoir Engineering ◽  
Predictive Analysis ◽  
Marketing Technology

Using Marketing Technology in Reservoir Engineering: The Application of Data Driven Predictive Analysis on a Mature Oil Field

2011 ◽  
Author(s):  
Ellen Bertina Zijlstra ◽  
Gael Riethmuller ◽  
Susanne Schaeftlein ◽  
Salima Saif Al Mahruqi ◽  
Ali Naamani ◽  
...  
Keyword(s):  
Oil Field ◽  
Data Driven ◽  
Reservoir Engineering ◽  
Predictive Analysis ◽  
Marketing Technology

Friction Torque and Leakage Based Data-Driven Approach for Rotary Seal Prognostics in Manufacturing Industry

2019 ◽  
Author(s):  
Madhumitha Ramachandran ◽  
Zahed Siddique
Keyword(s):  
Manufacturing Industry ◽  
Friction Torque ◽  
Operating Conditions ◽  
Oil Field ◽  
Data Driven ◽  
Sensor Technology ◽  
Physical Parameters ◽  
Direct Estimation ◽  
Rotary Seal ◽  
Wide Range

Abstract Rotary seals are found in many manufacturing equipment and machines used for various applications under a wide range of operating conditions. Rotary seal failure can be catastrophic and can lead to costly downtime and large expenses; so it is extremely important to assess the degradation of rotary seal to avoid fatal breakdown of machineries. Physics-based rotary seal prognostics require direct estimation of different physical parameters to assess the degradation of seals. Data-driven prognostics utilizing sensor technology and computational capabilities can aid in the in-direct estimation of rotary seals’ running condition unlike the physics-based approach. An important aspect of data-driven prognostics is to collect appropriate data in order to reduce the cost and time associated with the data collection, storage and computation. Seals in machineries operate in harsh conditions, especially in the oil field, seals are exposed to harsh environment and aggressive fluids which gradually reduces the elastic modulus and hardness of seals, resulting in lower friction torque and excessive leakage. Therefore, in this study we implement a data-driven prognostics approach which utilizes friction torque and leakage signals along with Multilayer Perceptron as a classifier to compare the performance of the two metrics in classifying the running condition of rotary seals. Friction torque was found to have a better performance than leakage in terms of differentiating the running condition of rotary seals throughout its service life. Although this approach was designed for seals in oil and gas industry, this approach can be implemented in any manufacturing industry with similar applications.

Integrated Automation and Data-Driven Workflow for CO2 Project Management – Case Study from a Smart Oil Field in the Middle-East

2021 ◽  
Author(s):  
Erismar Rubio ◽  
Mohamed Yousef Alklih ◽  
Nagaraju Reddicharla ◽  
Abobaker Albelazi ◽  
Melike Dilsiz ◽  
...  
Keyword(s):  
Project Management ◽  
Real Time ◽  
Reservoir Management ◽  
Oil Field ◽  
Data Driven ◽  
Co2 Injection ◽  
Well Performance ◽  
Potential Cost ◽  
Integrated Automation ◽  
Well Pattern

Abstract Automation and data-driven models have been proven to yield commercial success in several oil fields worldwide with reported technical advantages related to improved reservoir management. This paper demonstrates the implementation of an integrated workflow to enhance CO2 injection project performance in a giant onshore smart oil field in Abu Dhabi. Since commissioning, proactive evaluation of the reservoir management strategy is enabled via smart-exception-based surveillance routines that facilitate reservoir/pattern/well performance review and supporting the decision making process. Prolonging the production sustainability of each well is a key pillar of this work, which has been made more quantifiable using live-tracking of the produced CO2 content and corrosion indicators. The intensive computing technical tasks and data aggregation from different sources; such as well testing and real time production/injection measurements; are integrated in an automatic workflow in a single platform. Accordingly, real-time visualizations and dashboards are also generated automatically; to orchestrate information, models and multidisciplinary knowledge in a systematic and efficient manner; allowing engineers to focus on problematic wells and giving attention to opportunity generation in a timely manner. Complemented with numerical techniques and other decision support tools, the intelligent system data-driven model assist to obtain a reliable short-term forecast in a shorter time and help making quick decisions on day-to-day operational optimization aspects. These dashboards have allowed measuring the true well/pattern performance towards operational objectives and production targets. A complete set of KPI's has helped to identify well health-status, potential risks and thus mitigate them for short/long term recovery to obtain an optimum reservoir energy balance in daily bases. In case of unexpected well performance behaviors, the dashboards have provided data insights on the root causes of different well issues and thus remedial actions were proposed accordingly. Maintaining CO2 miscibility is also ensured by having the right pressure support around producers, taking proactive actions from continues evaluation of producer-injector connectivity/interdependency, improving injection/production schedule, validating/tuning streamline model based on surveillance insights, avoiding CO2 recycling, optimizing data acquisition plan with potential cost saving while taking preventive measures to minimize well/facility corrosion impact. In this work, best reservoir management practices have been implemented to create a value of 12% incremental oil recovery from the field. The applied methodology uses an integrated automation and data-driven modeling approach to tackle CO2 injection project management challenges in real-time.

Introduction of Near-Bit Geosteering Drilling Technology and Instrument Application in Thin Oil Exploration

2011 ◽  
Vol 228-229 ◽  
pp. 889-893
Author(s):  
Chun Hua Zhang ◽  
Guo Ying Meng ◽  
Guang Hua Liu ◽  
Li Min Sheng ◽  
Yan Fu Huang
Keyword(s):  
Development Trend ◽  
Oil Field ◽  
Reservoir Engineering ◽  
High Tech ◽  
Oil Exploration ◽  
Hydrocarbon Reservoir ◽  
Technology Applications ◽  
Drilling Technology ◽  
Drilling System ◽  
And Control

The near-bit geosteering drilling technology is a new high-tech .It integrats the techniques of drilling, logging and reservoir engineering. The paper introduced the application of this technology in oil exploration. CGDS is a kind of production of this technology applications. The paper introduced the structure ,function, parameters of CGDS production and its application in oil field. CGDS is recognized as the high and new technique in 21 century which ensures you to achieve the best place of a wellbore within a reservoir by providing geological, engineering parameter measurements and while drilling monitoring. With excellent ability of identification of hydrocarbon reservoir and direction guide, CGDS assists you to adjust and control the wellbore trace in real time according to the information of formation features. Through the introduction of this instrument,We can know more about the near-bit geosteering drilling technology The application prospect and development trend of the geosteering drilling system were also discussed.

scholarly journals BIM and Data-Driven Predictive Analysis of Optimum Thermal Comfort for Indoor Environment

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4401
Author(s):  
Vincent J.L. Gan ◽  
Han Luo ◽  
Yi Tan ◽  
Min Deng ◽  
H.L. Kwok
Keyword(s):  
Machine Learning ◽  
Mechanical Ventilation ◽  
Boundary Conditions ◽  
Thermal Comfort ◽  
Indoor Environment ◽  
Natural Ventilation ◽  
Data Driven ◽  
Predictive Analysis ◽  
Comfort Index ◽  
Indoor Thermal Comfort

Mechanical ventilation comprises a significant proportion of the total energy consumed in buildings. Sufficient natural ventilation in buildings is critical in reducing the energy consumption of mechanical ventilation while maintaining a comfortable indoor environment for occupants. In this paper, a new computerized framework based on building information modelling (BIM) and machine learning data-driven models is presented to analyze the optimum thermal comfort for indoor environments with the effect of natural ventilation. BIM provides geometrical and semantic information of the built environment, which are leveraged for setting the computational domain and boundary conditions of computational fluid dynamics (CFD) simulation. CFD modelling is conducted to obtain the flow field and temperature distribution, the results of which determine the thermal comfort index in a ventilated environment. BIM–CFD provides spatial data, boundary conditions, indoor environmental parameters, and the thermal comfort index for machine learning to construct robust data-driven models to empower the predictive analysis. In the neural network, the adjacency matrix in the field of graph theory is used to represent the spatial features (such as zone adjacency and connectivity) and incorporate the potential impact of interzonal airflow in thermal comfort analysis. The results of a case study indicate that utilizing natural ventilation can save cooling power consumption, but it may not be sufficient to fulfil all the thermal comfort criteria. The performance of natural ventilation at different seasons should be considered to identify the period when both air conditioning energy use and indoor thermal comfort are achieved. With the proposed new framework, thermal comfort prediction can be examined more efficiently to study different design options, operating scenarios, and changeover strategies between various ventilation modes, such as better spatial HVAC system designs, specific room-based real-time HVAC control, and other potential applications to maximize indoor thermal comfort.

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