Integrated Work Flow Aids Data Digitization, Management for Offshore Drilling

2021 ◽  
Vol 73 (10) ◽  
pp. 49-50
Author(s):  
Chris Carpenter
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Data Management ◽  
Real Time ◽  
Oil And Gas ◽  
Private Cloud ◽  
Offshore Drilling ◽  
Drilling Operation ◽  
Management Platform ◽  
Drilling Operations

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 202290, “Digital Documentation and Data Management for Offshore Drilling,” by Zhong Cheng, SPE, Xi’an Shiyou University and CNOOC, and Rongqiang Xu and Xiaolong Yu, CNOOC, et al., prepared for the 2020 SPE Asia Pacific Oil and Gas Conference and Exhibition, originally scheduled to be held in Perth, Australia, 20–22 October. The paper has not been peer reviewed. The industry is expending significant effort into using instrumentation and software to optimize operations in all domains for exploration and production to move toward the digital oil field. The complete paper describes an integrated geological-engineering data-management project covering all aspects of well-engineering work flows, with the objective of providing a continuous improvement platform to users. Introduction CNOOC has spent more than 20 years on the progression of information construction. A private cloud platform was completed in 2018, and the characteristics of oil and gas data and critical storage-management technologies were studied systematically. At the same time, nearly 20 kinds of drilling- operation analysis software have been developed independently. From the perspective of engineering technology, these provide real-time monitoring, remote decision-making, technical training, and other information resource services and support for offshore drilling operations. However, the following problems restrict the efficient operation of such projects: - Because of the lack of a unified data-integration-application platform, data sharing has not yet been realized. - In the process of real-time monitoring and remote decision-making, more engineering information based on drilling operations lacks the support of geomechanical data. - The knowledge base and case library to guide the prevention and handling of drilling-operation accidents have not been established. System-Target Analysis The design goals of the platform are embodied in three aspects: function, safety, and operability, while system performance requirements are summarized as adaptability, response speed, scalability, maintainability, and the effective-ness of failure-handling mechanisms. According to the functional requirements of different users for offshore-drilling cloud technical services, users generally are divided into three categories: headquarters decision-making managers, drilling-operation project teams, and system-operation and maintenance-service providers. System Construction Goals and Architecture Construction Goals - Chief among these was to build a geological-engineering integrated data-management platform. Another important goal was to build a case-management platform. An intelligent search engine is established to retrieve the corresponding disposal knowledge through a comprehensive information model. A knowledge-management subsystem is established, and users are linked with internal knowledge-management processes with the help of the cloud. The specific operation process is carried out in the private cloud, and the results are fed back to the user through the human/computer interface.

Impact of Data Quality on Well Operations: Case Study of Work-over Operations

2016 ◽  
Author(s):  
Alfred Enyekwe ◽  
Osahon Urubusi ◽  
Raufu Yekini ◽  
Iorkam Azoom ◽  
Oloruntoba Isehunwa
Keyword(s):  
Data Quality ◽  
Data Management ◽  
Real Time ◽  
Data Management System ◽  
Quality Data ◽  
Process Safety ◽  
Well Control ◽  
Drilling Operations ◽  
Short Time

ABSTRACT Significant emphasis on data quality is placed on real-time drilling data for the optimization of drilling operations and on logging data for quality lithological and petrophysical description of a field. This is evidenced by huge sums spent on real time MWD/LWD tools, broadband services, wireline logging tools, etc. However, a lot more needs to be done to harness quality data for future workover and or abandonment operations where data being relied on is data that must have been entered decades ago and costs and time spent are critically linked to already known and certified information. In some cases, data relied on has been migrated across different data management platforms, during which relevant data might have been lost, mis-interpreted or mis-placed. Another common cause of wrong data is improperly documented well intervention operations which have been done in such a short time, that there is no pressure to document the operation properly. This leads to confusion over simple issues such as what depth a plug was set, or what junk was left in hole. The relative lack of emphasis on this type of data quality has led to high costs of workover and abandonment operations. In some cases, well control incidents and process safety incidents have arisen. This paper looks at over 20 workover operations carried out in a span of 10 years. An analysis is done on the wells’ original timeline of operation. The data management system is generally analyzed and a categorization of issues experienced during the workover operations is outlined. Bottlenecks in data management are defined and solutions currently being implemented to manage these problems are listed as recommended good practices.

Rapid Development of Multi-Source Heterogeneous Drilling Data Service System

2021 ◽  
Author(s):  
Zhong Cheng ◽  
Rongqiang Xu ◽  
Jianbing Chen ◽  
Ning Li ◽  
Xiaolong Yu ◽  
...  
Keyword(s):  
Data Management ◽  
Management System ◽  
Service System ◽  
Third Party ◽  
Data Management System ◽  
Data Service ◽  
Offshore Drilling ◽  
The Third ◽  
Drilling Data ◽  
Drilling Operations

Abstract Digital oil and gas field is an overly complex integrated information system, and with the continuous expansion of business scale and needs, oil companies will constantly raise more new and higher requirements for digital transformation. In the previous system construction, we adopted multi-phase, multi-vendor, multi-technology and multi-method, resulting in the problem of data silos and fragmentation. The result of the data management problems is that decisions are often made using incomplete information. Even when the desired data is accessible, requirements for gathering and formatting it may limit the amount of analysis performed before a timely decision must be made. Therefore, through the use of advanced computer technologies such as big data, cloud computing and IOT (internet of things), it has become our current goal to build an integrated data integration platform and provide unified data services to improve the company's bottom line. As part of the digital oilfield, offshore drilling operations is one of the potential areas where data processing and advanced analytics technology can be used to increase revenue, lower costs, and reduce risks. Building a data mining and analytics engine that uses multiple drilling data is a difficult challenge. The workflow of data processing and the timeliness of the analysis are major considerations for developing a data service solution. Most of the current analytical engines require more than one tool to have a complete system. Therefore, adopting an integrated system that combines all required tools will significantly help an organization to address the above challenges in a timely manner. This paper serves to provide a technical overview of the offshore drilling data service system currently developed and deployed. The data service system consists of four subsystems. They are the static data management system including structured data (job report) and unstructured data (design documentation and research report), the real-time data management system, the third-party software data management system integrating major industry software databases, and the cloud-based data visual application system providing dynamic analysis results to achieve timely optimization of the operations. Through a unified logical data model, it can realize the quick access to the third-party software data and application support; These subsystems are fully integrated and interact with each other to function as microservices, providing a one-stop solution for real-time drilling optimization and monitoring. This data service system has become a powerful decision support tool for the drilling operations team. The learned lessons and gained experiences from the system services presented here provide valuable guidance for future demands E&P and the industrial revolution.

Building Intelligent Cities

2021 ◽  
pp. 1-27
Author(s):  
Vijayaraghavan Varadharajan ◽  
Akanksha Rajendra Singh
Keyword(s):  
Decision Making ◽  
Resource Allocation ◽  
Data Management ◽  
Real Time ◽  
Smart Cities ◽  
Technology Infrastructure ◽  
Intelligent City ◽  
Intelligent Cities ◽  
Comprehensive Introduction

A city may be regarded as an intelligent city when its services to citizens are connected and it is able to obtain data from every aspect of its technology infrastructure to leverage it in real time for resource allocation, monitoring, management, and decision making. Cities around the globe are ambitiously leveraging the latest technologies to transform their infrastructures to better provision and manage the e-services. Although they are setting goals for focusing on the appropriate financing, long-term planning, developing technology stack, and advancing data management, governments need to further encompass all relevant guidelines towards right technology frameworks before commencing their intelligent city projects. This chapter provides a comprehensive introduction to intelligent cities, also known as smart cities, and the associated requirements. It also articulates the evolution of a typical city to a truly integrated, responsive, open, and connected intelligent city and the required underlying technologies.

Intelligent Pressure Control System for MPD

2021 ◽  
Author(s):  
Zhao Hui Song ◽  
Fu Qiang Li ◽  
Deng Pan Xie ◽  
John Zhu ◽  
Liam Zeng ◽  
...  
Keyword(s):  
Control System ◽  
Real Time ◽  
Pressure Control ◽  
Full Time ◽  
Time Data ◽  
Drilling Operation ◽  
Pressure Control System ◽  
Bottom Hole Assembly ◽  
Drilling Operations ◽  
Compact Design

Abstract MPD (Managed Pressure Drilling) is an important technique for challenging drilling operations especially in narrow operational windows. This paper is to introduce the IPC (Intelligent Pressure Control) system with super compact footprint, unique algorithm and IoT (Internet of Things) feature which bring operator a fresh understanding of MPD operation. IPC system is equipped with the ultra-compact MPD manifold (L11.75ft × W7.50ft × H9.08ft) with complete functionality of measurement & automatic control, benefit the operators on footprint reducing for limited field space. With the unique algorithm integrated in iPWD (Intellegent Pressure While Drilling) module, the real time downhole pressure data could be generated without any downhole PWD (Pressure While Drilling) sensor, the deviation between iPWD data and real PWD data is within 3%, which was proven in field operations. NEBULA system is an add-on feature for IPC system, using cloud and IoT technologies, it could track the equipment’s specific location, working status and parameters, providing statistical diagnosis based on data collected from field operations, which helps operators to make decisions quickly. The data uploaded to cloud could generate different reports based on end user’s requirements to analyze drilling operation challenges or difficulties. You can receive all data provided by NEBULA system on your cellphone and PC (Personal Computer) at any time anywhere. The compact design of IPC system manifold benefit the operator by minimizing the footprint in limited field space especially for offshore operations; iPWD module provides full time data during drilling operation regardless of connection or any pump off scenarios; also erases the need of PWD sensor on BHA(Bottom Hole Assembly). NEBULA system featured on IPC equipment generates different report based on real-time data received on site after cloud calculation and big data analysis, all data and report could be accessed via cellphone or PC at any time anywhere, which can be an upgraded intelligent features on conventional MPD technology.

scholarly journals A New Hybrid Analytical-Machine Learning Method for Real-Time ROP Modeling

2020 ◽  
Vol 11 (1) ◽  
pp. 7589-7604
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Machine Learning Method ◽  
Learning Method ◽  
Real Time Control ◽  
Rate Of Penetration ◽  
Drilling Operation ◽  
Time Control ◽  
Constant Coefficients ◽  
Drilling Operations

Real-time drilling optimization refers to operations and equipment that could minimize total drilling costs. Drilling speed that is called the rate of penetration (ROP) in the drilling industry can be used as a good indicator for the performance evaluation of the drilling operation. Real-time control for drilling ROP is limited to just a few controllable parameters during drilling operations, that is, WOB, RPM, and hydraulics. These parameters can be controlled from the surface by the driller in real-time. In the traditional methods of ROP modeling, an inflexible equation could be developed between some important effective drilling parameters such as weight on the bit or bit rotational speed and drilling rate of penetration. These models had a low degree of accuracy, and they were not applicable in the newly drilled wells even in the same field with an acceptable degree of accuracy. In this study, a new real-time continues-learning method for ROP modeling was developed. In this method, as the drilling operation gets starts and the drilling data reaches the surface, ROP modeling starts, and as the drilling continues, the model accuracy increases. For the method evaluation, 5 famous existing analytical drilling model was selected. Also, a new ROP model was developed in this work. All of these 6 models contain some constant coefficients that were obtained using a new machine learning method named Rain Optimization Algorithm. In the end, the accuracy of the models was compared. Results show that the presented method for ROP modeling is a very flexible method with a high degree of accuracy that can be easily used in any formation. Also, the newly presented model could increase the accuracy of ROP prediction from 75% to 81%.

Arctic Offshore Engineering—An Overview

1983 ◽  
Vol 20 (04) ◽  
pp. 323-331
Author(s):  
Peter G. Noble
Keyword(s):  
Oil And Gas ◽  
State Of The Art ◽  
Canadian Arctic ◽  
Marine Riser ◽  
Offshore Drilling ◽  
Engineering Technology ◽  
Offshore Engineering ◽  
New Concepts ◽  
Drilling Operations ◽  
Ice Forces

The paper examines the state of the art of offshore drilling operations in the ice-infested Beaufort Sea region of the Canadian Arctic. As exploration and drilling proceed farther and farther from the shoreline into deeper and more hostile waters, new concepts are evolving in Arctic engineering technology to permit the economical recovery of vast resources of oil and gas over an extended season. Several of these innovations, including a deep-hulled drillship designed to protect the marine riser and reduce ice forces, artificial islands of the caisson type, and the use of massive icebreaking dredges, are described and illustrated.

Prediction Model for Morphological Distribution and Evolution of Submarine Cuttings Piles in Offshore Drilling

SPE Journal ◽  
2021 ◽  
pp. 1-16
Author(s):  
Zhi Zhang ◽  
Baojiang Sun ◽  
Zhiyuan Wang ◽  
Shikun Tong ◽  
Bing Guo ◽  
...  
Keyword(s):  
Flow Field ◽  
Transport Process ◽  
Oil And Gas ◽  
Ocean Current ◽  
Offshore Drilling ◽  
Oil And Gas Resources ◽  
Drilling Operations ◽  
Offshore Oil And Gas ◽  
Offshore Oil ◽  
Morphological Distribution

Summary Offshore oil and gas has effectively alleviated the global shortage of oil and gas resources, and drilling operations are becoming increasingly frequent. However, the cuttings discharged during surface drilling are transported and deposited to form cuttings piles, which pose a serious threat to the marine ecological environment. In this study, we consider the randomness and uncertainty of cuttings movement to divide the transport process into parabola and collision motion between the moving particles and slope particles after falling on the slope surface of cuttings piles. Through specific analysis of the stress state of a single particle in the transport process and changes in momentum distribution of the particle swarm, the evolution model of the morphological distribution of cuttings piles and the nearby flow field is established. This model can quantitatively analyze the evolution law of the morphological distribution of cuttings piles under the action of ocean current and the disturbance law of the flow field near the cuttings piles caused by the invasion of cuttings particles. Comparing the measured data at an offshore drilling field and prediction results of the model of Sun et al. (2020), the relative error of the model amounts to less than 15%, which demonstrates its rationality. The simulation results show that the morphological distribution of cuttings piles and the nearby flow field change significantly under the action of ocean current, and the intensity of evolution is related to the current velocity and cuttings size, which is of great significance for the quantitative analysis of the evolution of cuttings piles under the action of ocean currents and accurate prediction of their morphological distribution.

ZAMREN Big Data Management (ZAMBiDM) Envisaging Efficiency and Analytically Manage IT Resources

Web Services ◽  
2019 ◽  
pp. 1802-1811
Author(s):  
Jameson Mbale
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Management ◽  
Real Time ◽  
Economic Gain ◽  
Teaching And Research ◽  
Strategic Actions ◽  
Huge Data ◽  
It Resources

The ZAMREN member institutions deal with heterogeneous teaching and research materials drawn from all walks-of-life such as industry, and NRENs world over. To deal with such huge data that is in terabits for academic and economic gain becomes a mammoth task to manipulate, process, store and analyse. It is in view of that the ZAMREN Big Data and Data Management, in this work abbreviated as ZAMBiDM, is envisaged to collectively gather relevant heterogeneous large volumes of a wide variety of data from all sectors of economy. The data would be analytically managed in storage, processing and obtaining actionable insight real-time as a way to solve high-value skilled academic and industrial business problems, in order to prepare graduates for competitive future workforce. The data would be collected from all line-ministries of Zambia such as education, agriculture, health, mining, lands, communications, commerce, including industries and NRENs worldwide and be analytically analysed to exploit strategic actions that would enhance decision making in executing relevant tasks.

ZAMREN Big Data Management (ZAMBiDM) Envisaging Efficiency and Analytically Manage IT Resources

2016 ◽  
pp. 55-67
Author(s):  
Jameson Mbale
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Management ◽  
Real Time ◽  
Economic Gain ◽  
Teaching And Research ◽  
Strategic Actions ◽  
Huge Data ◽  
It Resources

The ZAMREN member institutions deal with heterogeneous teaching and research materials drawn from all walks-of-life such as industry, and NRENs world over. To deal with such huge data that is in terabits for academic and economic gain becomes a mammoth task to manipulate, process, store and analyse. It is in view of that the ZAMREN Big Data and Data Management, in this work abbreviated as ZAMBiDM, is envisaged to collectively gather relevant heterogeneous large volumes of a wide variety of data from all sectors of economy. The data would be analytically managed in storage, processing and obtaining actionable insight real-time as a way to solve high-value skilled academic and industrial business problems, in order to prepare graduates for competitive future workforce. The data would be collected from all line-ministries of Zambia such as education, agriculture, health, mining, lands, communications, commerce, including industries and NRENs worldwide and be analytically analysed to exploit strategic actions that would enhance decision making in executing relevant tasks.

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