Future Workforce Education through Big Data Analysis for Drilling Optimization

With the current market's high prices for drilling units and sophisticated directional and formation assessment services, low systems reliability or poor directional performance in the execution of complex wells (ERD, HPHT, Deep Water applications) can spell costly Non Productive Time (NPT) for operators. Over the past two decades, many Bottom Hole Assembly (BHA) models have been developed to address the directional performance issue, but their application in an operational environment has generally failed for lack of strong associated BHA analysis methodologies. This paper presents a unique automated methodology for Post Analysis of BHA directional behaviour. It is based on modern 3D analytical models, combining BHA, BIT and formation effects with highly flexible data management. Implemented in user-friendly BHA management software, it can be used to define a BHA run segmentation and to determine the influence of the BHA settings in particular geological formation blocks. The Post Analysis clearly displays the final results of the BHA (objectives and performances versus prediction window), highlighting any problematic intervals drilled. Already, the methodology has solved challenging directional performances issues, generating significant savings and optimising the learning curve process. The field applications referred to in the paper have demonstrated that, without a proper methodology, a model offers little added value. The examples discussed here from Total E&P affiliates deal respectively with Rotary, Rotary Steerable System (RSS) and Steerable Motor (SM) directional BHAs. Case studies have led to BHA and bit design modifications. The results were well beyond expectations, ensuring good well positioning through the reservoir by improving BHA stability and manoeuvrability. Efficient drilling (reduced sliding, improved hole quality) was also achieved with a single SM BHA in 17 1/2" applications during kick-off, and successive build-up and slant drilling intervals. The methodology and its associated BHA Management® software have proved an undeniably valuable tool for the drilling community. The model is also run at the well Pre Engineering stage to check BHA design proposals by making multiple sensitivity analyses and by fine-tuning solutions to the operational context. Finally, in an industry with high personnel turnover, the software's global data management system ensures good capitalization of know-how and ongoing learning curve benefits.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 189910, “Operational Improvements Using a Coiled-Tubing-Telemetry System for a Complex Milling Operation in Shut-In Conditions,” by P. Correa, D. Parra, S. Craig, SPE, S. Livescu, SPE, A. Yeginbayev, and Z. Nadirov, Baker Hughes, a GE Company, prepared for the 2018 SPE/ICoTA Coiled Tubing and Well Intervention Conference and Exhibition, The Woodlands, Texas, USA, 27–28 March. The paper has not been peer reviewed. When a new horizontal well in Asia was incapable of unassisted flow, coiled tubing (CT) was selected for the perforation and stimulation intervention. Mechanical isolation was required to ensure that the stimulation fluids entered only the new zones. Accurate depth control was required for three runs: setting two composite bridge plugs (CBPs), deploying CT-conveyed perforating guns for opening two intervals, and milling out the two CBPs without taking returns to surface. For the first time, a tension, compression, and torque (TCT) subassembly was used to improve the milling operation. Introduction CT telemetry (CTT) systems have proved to be effective in enhancing operations by providing real-time monitoring of downhole data including depth, pressure, temperature, force, and torque. For data transmission from downhole sensors to surface, these telemetry systems can use either insulated wire or optical fiber. With insulated wire, power can be transferred from surface to the downhole sensors. With optical fiber, downhole batteries are required in the bottomhole assemblies (BHAs) to power the downhole sensors. When compared with braided systems using the 0.125-in. insulated electrical cable (Fig. 1), the main advantages of the CTT system are its low weight and the fact that balls can be pumped through the CT. A small wire has a minimal effect on the CT cross-sectional area. Balls up to 7/8 in. in diameter do not become stuck, and there is a minimal effect on the CT fluid pressure drop. Description of the Wire CTT System The 0.125-in.-wire CTT system has several components, such as surface hardware and software; the ⅛-in. insulated electrical conductor inside the CT string; and a BHA with different sizes, including 2.125, 2.875, and 3.5 in. The 2.875-in. BHA with three subassemblies is shown in Fig. 2. The 2.875-in. sensor assembly has the following components: Tubing end connector Head that secures the wire downhole, seals the exterior of the wire, and provides downhole electrical connection 0.875-in. ball-operated wire-release mechanism Double-flapper check-valve housing Electronic module with a casing collar locator (CCL) and pressure/temperature sensors (internal/external) Motorhead section, where a ¾-in. ball-release mechanism is located followed by a circulating port activated with 0.625-in. balls Burst-disk section enabling 0.5-in. balls to pass through to activate downhole tools

The use of drilling automation is accelerating, mostly in the area of rate of penetration (ROP) enhancement. Autonomous directional drilling is now a high focus area automating the drilling operations. The potential impact is immense as 93% of the active rigs in the US are drilling directional or horizontal wells. The 2018-2019 DSATS-led international Drillbotics® Student Competition includes automated directional drilling. This paper discusses the detailed design of the winning team. It presents the surface equipment, downhole tools, data and control systems, and lessons learned. The Drilling Systems Automation Technical Section (DSATS) of SPE organizes the annual Drillbotics competition for the university teams to design and develop laboratory-scale drilling rigs. The competition requires each team to create unique downhole sensors to allow automated navigation to drill a directional hole. Student teams have developed new rig configurations to enable several unique steering methods that include a rotary steering system and small-scale downhole motors with a bent sub. The most significant challenge was creating a functional downhole motor to fit within a 1.25-inch (3.18 cm) diameter wellbore. Besides technical issues, teams must demonstrate what they have learned about bit/formation interaction and the physics of steering. Additionally, they must deal with budgets and funding, procurement and delivery delays and overall project management. This required an integrated multidisciplinary approach and a major redesign of the rig components. The University of Oklahoma team (winner) made significant changes to its existing rig to drill directional holes. The design change was introduced to optimize the performance of the bottom hole assembly (BHA) and allow directional drilling. The criteria for selecting the BHA was hole size, BHA dynamics, a favorable condition for downhole sensors, precise control of drilling parameters, rig mobility, safety, time constraints, and economic practicality. The result is an autonomous drilling rig that drills a deviated hole toward a defined target through a 2 × 2 × 1 ft (60.96 × 60.96 × 30.48 cm) sandstone block (i.e. rock sample) without human intervention. The rig currently employs a combination of discrete and dynamic modeling from experimentally determined control parameters and closed-loop feedback for well-trajectory control. As demonstrated during the competition, the research and development conducted by the team go well beyond the textbooks. The novelty of our design is the use of a small-scale cable-driven downhole motor with a bent sub and quick-connect type swivel system. This is intended to replicate the action of a mud motor within the limits of the borehole diameter. This article presents details of the rig components and their specifications, and the problems faced during the design, development, and testing. The paper demonstrates how a laboratory-scale rig can be used to study drilling dysfunctions and challenges. Building a downhole tool to withstand vibrations, water intrusion, magnetic interference, and electromagnetic noise are common difficulties faced by major equipment manufacturers.

Technology Focus Drilling-efficiency improvements, needed to ensure continuous reductions in project cycle time, require holistic drilling-system evaluations. Bottomhole assemblies (BHAs), as part of the drilling system, have dominating effects on the functionality and effectiveness of other system components. BHA design, usage, and monitoring must promote drilling-system compatibility, efficiency, and reliability. BHA design must provide or contribute to the creation of operational conditions that ensure efficient failure and removal of rocks. Additionally, it must enable the use of appropriate drilling parameters as needed by specific applications without compromising efficiencies of other drilling-system components. Research continues to investigate rock-failure mechanisms and the implications the processes have on drilling performance. Through this effort, it has been identified that different drilling parameters are needed for dissimilar rock types, even when rock hardness, hole sizes, well profiles, and hydraulic considerations are the same. In most cases, these findings dictate the need for different BHAs to accommodate appropriate parameter ranges and sweet spots. BHA usage must not create conditions that initiate or promote dynamic dysfunctions. Such a condition, while compromising the functional efficiencies of other drilling-system components, also promotes premature downhole-tool failures. The contributions of micro-doglegs to dynamic dysfunctions, excessive torque and drag, poor weight-on-bit transfer, and other issues have been known for a long time. Research into this area is providing a deeper understanding, together with findings that are bound to improve drilling-parameter management and overall drilling efficiency. Appropriate parameter ranges, and improved BHA compliance with regard to vibrations, improve drilling efficiency. These conditions, though beneficial, are not sufficient to ensure BHA reliability. Considering the effect that BHA failures (e.g., twistoff) have on project cycle time and operational costs, investigators have taken a deeper dive into this issue. With the “whys” and “hows” of BHA failures identified, work flows have been developed to address these reliability issues. Ensuring BHA efficiency and reliability is critical for drilling performance. These requirements must thread through BHA design, usage, and monitoring. Yes, the industry has the tools and know-how, and it can be done. Recommended additional reading at OnePetro: www.onepetro.org. SPE 183870 A Modified Three-Point-Contact Approach for Dogleg-Severity Modeling by Hossam Gharib, Halliburton, et al. SPE 183037 Testing and Characterization of Shock and Vibration Loads To Enhance Drilling-Tool Reliability and Efficiency by Dennis Heinisch, Baker Hughes, a GE company, et al. OTC 27575 Applying Real-Time Data Analytics To Detect Underreamer Failure Early During Hole Enlargement While Drilling in Deepwater Gulf of Mexico by Xianping Wu, Shell, et al.

Borehole quality is a crucial aspect of wellbore construction, impacting drilling and completion execution and long-term well reliability in terms of zonal isolation and casing integrity. However, developing key performance indicators and requirements for borehole quality has proven difficult since there are few direct measurements of borehole characteristics other than surveys. This paper explores the use of ultra-high frequency acoustic borehole image logs and caliper measurements to develop methods to quantify effects during drilling operations, such as micro-tortuosity, salt creep, and other observed characteristics, which could prove useful in defining wellbore quality in the drilling phase. The role of the repeatable process of drilling a stand with different bottom hole assembly (BHA) designs as motorized rotary steerable systems (RSS) and conventional steerable systems (CSS) will be mapped to borehole quality impact with downhole measurements. Drilling operations and methods will be linked with the resulting borehole measurements and the relationship in terms of correlation and causality examined. This case study documents the application of the processes and tools for vertical and lateral intervals of horizontal wells in the different basins of North America. Results of different states of drilling operations such as sliding, rotating, and reaming, drilling dysfunctions, BHA changes, drilling practices, and rock characteristics will be presented. The paper's conclusions use this approach to determine the needs and methods of wellbore quality measurement and requirements for drilling and completions and potential life of well implications. This paper builds on the drilling mechanics process and downhole measurements to determine usable borehole quality key performance indicators that can be applied as additional wellbore quality management methods.

For more than 50 years, coiled tubing (CT) has been an intervention technology primarily used to maintain or increase production. In the last 10 years, CT telemetry systems have been used for such applications as milling, stimulation, well cleanouts, gas lifting, camera services, logging and perforating, increasing certainty, improving safety and efficiency, and reducing time and cost. In this paper, a review of a CT telemetry system with 2 ⅛-in. tube wire, including the technology development and field application, is presented for the first time. Unlike conventional CT for which surface-measured parameters, such as CT weight and length and pumping pressure, are the only parameters available to monitor the operation's progress, CT telemetry systems provide real-time monitoring of downhole data such as pressure, temperature, depth, etc. The CT telemetry system described in this paper consists of the surface hardware and software, a 2 ⅛-in. tube wire inside the CT connecting the surface equipment and the downhole tools and sensors, and a versatile bottomhole assembly, designed in three sizes (i.e., 2 ⅛-, 2 ⅞-, and 3 ½-in.). The 2 ⅛-in. tube wire has the dual purpose to power up the downhole sensors and to transfer the real-time downhole data to surface. The sensors available are a casing collar locator (CCL), two pressure and temperature transducers (capable to measure downhole data inside and outside of the tool), and tension, compression, and torque gauges. In addition, cameras with front and lateral views and flow-through capabilities could be used. One of the advantages of this CT telemetry system is its versatility: switching between applications is as simple as only changing parts of the bottomhole assembly, significantly reducing the operational time and cost and increasing safety. Another advantage stems from the downhole data certainty in real time, as the CT field crew can promptly intervene based on dynamic downhole events. A state-of-the-technology review of the 2 ⅛-in. tube wire CT telemetry system is presented for the first time. The many benefits of the real-time monitoring of the downhole parameters during such CT applications as logging, zonal isolation, collapsed casing identification, scale removal, cleanout and perforation, milling, confirmation of jar activation during fishing jobs, etc., are also summarized. Many of these applications were performed together and the real-time monitoring of downhole data increased the job efficiency, control and safety and reduced the operational costs by simplifying the operational procedures and equipment. The paper summarizes the results stemming from 10 years of global experience with the 2 ⅛-in. tube wire CT telemetry system. A new case history involving the 2 ⅛-in. tube wire CT telemetry system and a vibratory tool is presented for the first time. As currently there is a strong inertia to automate the drilling operations, all details presented in this paper show that the CT telemetry systems are poised to become standard technologies for all CT operations in the not-so-distant future.

The unprecedented proliferation of miniaturized sensors and intelligent communication, computing, and control technologies have paved the way for the development of the Industrial Internet of Things. The IIoT incorporates machine learning and massively parallel distributed systems such as clouds, clusters, and grids for big data storage, processing, and analytics. In IIoT, end devices continuously generate and transmit data streams, resulting in increased network traffic between device-cloud communication. Moreover, it increases in-network data transmissions. requiring additional efforts for big data processing, management, and analytics. To cope with these engendered issues, this article first introduces a novel concentric computing model (CCM) paradigm composed of sensing systems, outer and inner gateway processors, and central processors (outer and inner) for the deployment of big data analytics applications in IIoT. Second, we investigate, highlight, and report recent research efforts directed at the IIoT paradigm with respect to big data analytics. Third, we identify and discuss indispensable challenges that remain to be addressed for employing CCM in the IIoT paradigm. Lastly, we provide several future research directions (e.g., real-time data analytics, data integration, transmission of meaningful data, edge analytics, real-time fusion of streaming data, and security and privacy).

Big data and data analytics are currently the buzzwords in both academia and industry to become data driven. Big data has been the trending topic in the accounting industry also. Big data and data analytics will have an important impact on accounting and accountants. Big data will improve the quality of accounting information and the accounting profession will continue to provide real-time and dynamic information to assist in decision-making. The purpose of this research is to investigate the impact of big data and big data analytics in accounting. Data analytics is one of the most recent developments in the accounting context. This study is qualitative in nature and adopted a literature review methodology to gain a better understanding of the study area. This literature review seeks to provide a description and evaluation of the impact of big data analytics on accounting. This research found that big data presents great opportunities for decision making in accounting and risks analysis, which indicated that companies could improve their performance, measure performance, manage risks and allow effective real-time decision-making with data analytics. This research revealed that accountants can create more value in a world of big data analytics and encourages accountants to get started with big data to find answers to risks in business operations as well as understand financial performance. It shows that relying on big data analytics will open new possibilities for accountants. This study contributes to the research literature in the area of big data analytics on accounting. The limitations of this study are that it utilizes few recent peer reviewed articles in the general accounting practice, therefore not exhaustive in describing how big data and big data analytics impacts accounting.

In the rapidly evolving landscape of data mining, the effective extraction of valuable insights from large datasets is paramount. This survey paper investigates the pivotal roles of data visualization and analytics in the intricate process of data mining abstraction. We delve into the symbiotic relationship between these two components, examining how they synergistically contribute to the extraction, representation, and interpretation of meaningful patterns and trends within complex datasets. The survey begins by elucidating the fundamental concepts of data mining abstraction and the significance of distilling actionable knowledge from raw data. It subsequently explores the multifaceted benefits of data visualization, elucidating its role in pattern identification, insight generation, and seamless communication of findings to diverse stakeholders.In parallel, the paper navigates through the landscape of data analytics, unraveling its diverse methods such as descriptive analytics, predictive analytics, and prescriptive analytics. Emphasis is placed on how these analytical techniques enhance the abstraction process, providing statistical rigor and predictive power to unveil hidden insights.The integration of data visualization and analytics is a focal point, showcasing their collective impact on various stages of data mining. From exploratory data analysis (EDA) for initial dataset understanding to the evaluation of mining models, the survey illuminates the collaborative nature of these components. Interactive dashboards emerge as a powerful tool, allowing users to dynamically explore datasets, visualize trends, and perform real-time analytics. Key Words:Data visualization , Data analytics ,Data mining , Tools and Software , Metrics and Key Performance Indicators (KPIs).

This chapter provides a brief overview of the growing role played by big data, data analytics, and artificial intelligence (AI) in the accounting profession. The term 'Big Data' along with other trending topics such as 'Data Analytics' and 'Artificial Intelligence (AI)' have become buzzwords in the accounting profession in recent years. The skills of accounting professionals have evolved as technology has made rapid advances from using pencil and paper to typewriters and calculators, and eventually spreadsheets and accounting software. Data analytics in accounting is a relatively new skill set that is growing significantly in all areas of the accounting profession. Accounting professionals who can discern patterns and trends in big data and translate them into compelling strategic narratives will find themselves at the center of the twenty-first-century business world. Therefore, accounting professionals can capitalize on numerous opportunities in this rapidly evolving, disruptive, but ultimately advantageous environment by embracing big data, data analytics, and artificial intelligence (AI) to stay ahead of the competition.

Maximizing well productivity and improving drilling efficiency remains a major challenge while drilling horizontal wells in US-land unconventional shale plays in the last few years. Reducing drilling times and eliminating trips for the curve bottomhole assembly (BHA) requires a motor that can be rotated at high RPM in the vertical section while still achieving buildup rate (BUR) in the curve. The challenges of horizontal well drilling in US land led to the recent introduction of a steerable optimized design motor (ODM) with a short bit-to-bend (BTB) distance. These ODMs achieved higher BUR in the curves than the conventional motors at lower adjustable kick-off (AKO) sub angle. Although the planned dogleg severities (DLS) stayed at the similar level, drilling vertical and curve sections of the horizontal wells in the Niobrara shale unconventional play posed additional challenges: – Rotating the BHA in the vertical section with a high AKO angle – Dealing with formation challenges – Holding the toolface to achieve consistent BUR in the curve – Completing the vertical and curve sections in one run The introduction in 2013 of the latest-generation steerable motors (LGSM) with further reduced short BTB distance design helped the operator overcome these challenges. The new system significantly improved drilling performance with excellent directional control. This paper will discuss the design, testing and results of horizontal wells drilled using the LGSM in the Niobrara unconventional shale play.

Purpose. This research aims to investigate how small and medium-sized enterprises (SMEs) can strategically leverage key performance indicators (KPIs) to enhance their performance and competitiveness in the dynamic business landscape. Through a multifaceted research methodology, including secondary data analysis, case studies, comparative analysis, and future trends analysis, the study seeks to address the specific challenges faced by SMEs and provide actionable insights for optimizing performance through KPI utilization. Results. The findings of this research reveal the intricacies of SME performance challenges, ranging from financial constraints to operational complexities. Through a comprehensive examination of theoretical foundations and real-world case studies, the study demonstrates successful strategies used by SMEs in effectively utilizing KPIs. The comparative analysis identifies patterns and distinctions in KPI deployment across industries, enriching the understanding of tailored approaches. The forward-looking future trends analysis anticipates advancements in data analytics, artificial intelligence, and technological tools that will shape the landscape of SME performance measurement. Scientific novelty. This research contributes to the field by offering a nuanced exploration of challenges faced by SMEs and the transformative potential of KPIs within their unique operational environments. The comparative analysis and future trends exploration add a novel dimension, providing a forward-looking perspective on the evolving landscape of KPI utilization in SMEs. The research pioneers an investigation into emerging technologies and methodologies, positioning itself at the forefront of anticipating future trends in SME performance measurement. Practical value. The practical value of this research lies in its provision of actionable insights for SME leaders, policymakers, and researchers seeking to enhance the resilience and competitiveness of SMEs. By synthesizing theoretical foundations with real-world examples, the study offers practical guidance on addressing challenges and optimizing performance through KPI utilization. The exploration of tools, technologies, and future trends equips SMEs with the knowledge needed to navigate the intricate business landscape effectively.

The present study conducted a Systematic Literature Review (SLR) on the influence of Big Data in the definition of Key Performance Indicators (KPIs) in logistics and their impact on customer satisfaction. The reviewed literature showed numerous studies on the use of Big Data in logistics; however, there were few efforts that synthesized aspects such as defined KPIs, developed methods, and implemented tools. The objective was to explore and analyze how Big Data supported the definition of logistics KPIs and its influence on customer satisfaction. The methodology encompassed three stages: planning, implementation, and results, following Kitchenham's approach. A review protocol was established with key research questions and inclusion and exclusion criteria. Systematic searches were conducted in relevant databases, and pertinent studies were selected. The results revealed that the definition of KPIs varied according to context and industry; 23 KPIs, 9 Big Data methods, and 7 IT tools were identified. Finally, the conclusions emphasized the importance of adapting logistics KPIs to business priorities and the relevance of Big Data in their definition to improve both decision-making and customer satisfaction.

In mobile networks, handover (HO) is one of the most important and complex KPIs (Key Performance Indicators), which directly affect to Quality of Service (QoS), Quality of Experience (QoE), and mobility performance. Moreover, its configuration parameters such as handover thresholds and handover neighbor lists are the key factors for implementing network optimization such as load balancing and energy saving. In a study before, the authors proposed clustering and forecasting models using ML algorithms and Time Series models to cluster, forecast, and manage the HO behavior of a huge number of cells. In this study, on the other hand, the authors firstly investigated more network KPIs to analyze their relationship with HO KPIs, and then, proposed new clustering, forecasting, and abnormal detection models that are expected to make them much more comprehensive. Finally, the performances of the proposed models were evaluated by applying them to a real dataset collected from the HO KPIs and other KPIs of more than 6000 cells of a real network during the years, 2016 and 2017. The results showed that the study was successful in identifying the relationship among network KPIs and significantly improving the performance of the HO clustering, forecasting, abnormal detection models. Moreover, the study also introduced the integration of emerging technologies such as machine learning (ML), big data, software-defined network (SDN), and network functions virtualization (NFV) to establish a practical and powerful computing platform for future self-organizing networks (SON).

Evaluating maturity level of big data management and analytics in industrial companies