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Sour Rated 10,000-psi System High Temperature Gas Development Wells: A Journey of Optimization in North Malays Basin While Sustaining the Nation Gas Demand
Jong, Siaw Chuan (Hess Exploration and Production Malaysia B.V., Kuala Lumpur, Malaysia) | Aziz, Khairil Faiz Abdul (Hess Exploration and Production Malaysia B.V., Kuala Lumpur, Malaysia) | Goo, Jia Jun (Hess Exploration and Production Malaysia B.V., Kuala Lumpur, Malaysia) | Hiew, Ronnie (Hess Exploration and Production Malaysia B.V., Kuala Lumpur, Malaysia) | Strickland, Kenny (Hess Exploration and Production Malaysia B.V., Kuala Lumpur, Malaysia) | Hussin, Arief (Hess Exploration and Production Malaysia B.V., Kuala Lumpur, Malaysia) | Yusof, Khazimad (Hess Exploration and Production Malaysia B.V., Kuala Lumpur, Malaysia) | Macleod, Andy (Hess Exploration and Production Malaysia B.V., Kuala Lumpur, Malaysia) | Yusoff, Syukur (Hess Exploration and Production Malaysia B.V., Kuala Lumpur, Malaysia) | Chung, Chay Yoeng (Hess Exploration and Production Malaysia B.V., Kuala Lumpur, Malaysia) | Liew, Alex (Hess Exploration and Production Malaysia B.V., Kuala Lumpur, Malaysia)
Abstract High temperature, high carbon dioxide coupled with hydrogen sulfide contents, and rapid PPFG pressure ramp increase gas development well tends to cause high well capex for Operator. This well type typically needs high CRA material with at least a 10,000-psi rated system to complete. Offshore peninsular Malaysiaโs North Malay Basin (NMB)โs deep reservoirs also fall into the described category. This paper aims to share the optimization journey, applications, and learnings of the companyโs H.T. sour-rated 10Ksi gas development wells through several phases, besides fulfilling the gas delivery need for the country. In addition, engineering and operational optimizations are identified to reduce the wellโs time and cost without sacrificing the crewโs safety as the team focus. The company wells engineering team applied Lean approaches encompassing the complete Plan-Do-Check-Adjust cycle to achieve the optimization. Well data usage, lessons learned, collaboration, continuity, and striving for continuous improvements are the key factors to ensure good optimization results. Fit-for-purpose drilling and completions equipment design and application, rig offline capabilities planning, wellhead dummy hanger plug design for offline cementing, intervention-less production packer setting device, offline annulus nitrogen cushion fluids displacement and other applications will be explained in the paper. The paper explained the operational challenges, how and what optimizations applied to achieve excellent well performance compared to targets and previous campaigns. The wells team optimizations spread out from engineering to execution stages, including rolling out in-house talent of digitization and digitalization of well performance surveillance, in line with the industry's way forward. The recent campaign post optimization concluded with no safety incidents, below budgeted time and cost, low overall NPTs, and achieved first gas to meet the country's power generation demand. Open, collaborative, and proactive cross departments communications are the catalysts that contributed to the positive optimization journey's results.
- Asia > Malaysia > South China Sea (0.61)
- Asia > Malaysia > Kelantan > South China Sea > Gulf of Thailand (0.61)
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > Asia Government > Malaysia Government (0.46)
Cloud-Based Data Integration Between Geology & Drilling Software Using Python Tools
Mwansa, P. (ADNOC Onshore, Abu Dhabi, United Arab Emirates) | Hernandez, B. C. H. R. (CEGAL, Stavanger, Norway) | Grebe, S. (CEGAL, Stavanger, Norway) | Hassan, M. A. (OLIASOFT AS, Oslo, Norway) | Torsรฆter, A. (OLIASOFT AS, Oslo, Norway) | Jenssen, F. (OLIASOFT AS, Oslo, Norway)
Abstract The oil and gas industry generates vast amounts of data throughout its operations, from exploration to production. Collecting, connecting, and optimally utilizing this data is key to maximizing efficiency, accuracy, and access to new disruptive technologies. In a typical well-planning cycle, an engineer will spend significant amount of time looking for the data they require to do their jobs efficiently. The data are typically locked away in silos - trajectories in one data platform, Pore Pressure Gradient, Fracture Gradient or Targets in another, and so on. A major Middle Eastern NOC and Two Norwegian software service companies teamed up to develop Proof of Concept (PoC) for a new workflow that integrates subsurface and drilling data between on-premises Geology E&P software and Drilling software through a proprietary Python Tool plug-in and Python library. This integration enables a streamlined connection to a cloud-based drilling and well planning software, facilitating collaboration among teams involved in well planning. The project's key challenges are the lack of a standardized communication, integration, and automation of data flows between subsurface and drilling teams, as well as the inability of engineers to access necessary data due to scattered information and access restrictions. The project utilizes a proprietary data science suite, named Cegal's Prizm, which allows easy configuration to integrate data from various applications, sources, and platforms. A proprietary Python Tool is used to merge data from various application silos and data sources, enabling enriched investigation. The process involves connecting to the Geology E&P software retrieving domain objects using the proprietary Python Tool, and converting these domain data objects into common Python data structures. The project aims to develop an innovative workflow that provides easier access to data for experts throughout the organization, leading to better decision-making during the well-planning cycle. This not only makes it easier, but it also ensures collaboration between the G&G and Drilling teams involved in new well planning
- Europe > Norway (0.47)
- Asia > Middle East > UAE (0.47)
- Health, Safety, Environment & Sustainability > Security > Data and communications security (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Data security (0.95)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (0.88)
- (2 more...)
Planning and Execution of First Underbalanced Coiled Tubing Drilling in Adnocยดs Fields Utilizing First Successful Closed Loop System Globally
Osama, Mohamed (ADNOC Onshore, Abu Dhabi, UAE) | Sumaida, Ali Sulaiman Bin (ADNOC Onshore, Abu Dhabi, UAE) | Shahat, Ayman El (ADNOC Onshore, Abu Dhabi, UAE) | Mutawa, Ahmed Al (ADNOC Onshore, Abu Dhabi, UAE) | Almazrouei, Saeed (ADNOC Onshore, Abu Dhabi, UAE) | Saleh, Abdalla (ADNOC Onshore, Abu Dhabi, UAE) | Yousfi, Fawad Zain (ADNOC Onshore, Abu Dhabi, UAE) | Almteiri, Nama Ali (ADNOC Onshore, Abu Dhabi, UAE) | Baslaib, Mohamed (ADNOC Onshore, Abu Dhabi, UAE) | Mantilla, Alfonso (ADNOC HQ, Abu Dhabi, UAE) | Deshmukh, Rohit V. (ADNOC Onshore, Abu Dhabi, UAE) | Solaiman, Tarek (ADNOC Onshore, Abu Dhabi, UAE) | Abdulsallam, Fouad (ADNOC Onshore, Abu Dhabi, UAE) | Ladmia, Abdelhak (ADNOC HQ, Abu Dhabi, UAE) | Rangel, Pedro (Slb, Abu Dhabi, UAE) | Rennox, John (Slb, Abu Dhabi, UAE) | Cui, Shuai (Slb, Abu Dhabi, UAE) | Jumagaliyev, Yerlan (Slb, Abu Dhabi, UAE) | Basha, Maged (Slb, Abu Dhabi, UAE)
Abstract The Operator planned and conducted Underbalanced Coiled Tubing Drilling (UBCTD), operations on 3 wells in Operator Onshore fields targeting tight sour gas carbonate reservoirs. The objectives of these operations were to evaluate the applicability of the technology in these fields, to understand requirements and methods of the technology and to evaluate the benefits of drilling the target formations in an underbalanced mode. As a preliminary step, the Operator conducted a feasibility study that flagged potential limitations to deploying UBCTD operations in existing wells, due to limitations on the completion design and other factors. All of this resulted in the plan to drill fit-for-purpose wells to the top of the reservoir to facilitate the deployment of the technique. These wells were completed with 5.5/4.5 in. Tubing and 7 in. Liner and left with a 100ft open hole interval from where CT drilling operations would later continue. The results of the feasibility study notwithstanding, additional detailed engineering work was performed in all aspects of the design by the operations team to ensure the success of the trial, including a review and validation of the available data and the feasibility to deliver the stated objectives (lateral length, underbalanced conditions, minimal flaring operations, drilling fluid re-circulation, etc.). As a result of this approach, all three wells were successfully drilled in underbalanced conditions and to the target lateral length of 4,000 ft. Well placement was facilitated using Biosteering techniques and continuous monitoring of the well performance vs. drilled footage, allowing steering decisions to be made in real-time to maximize the production of each lateral, resulting in outstanding production results of 3x the productivity of similar wells drilled conventionally, (after stimulation). This paper will detail the design process highlighting key engineering decisions and assumptions taken during the design process and comparing them to the actual behavior of the well and the impact of real-life constraints on the operational parameters. The base design and lessons learned from the project will serve as a launching pad for planning and efficiency gains for future UBCTD operations.
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > Asia Government > Middle East Government > UAE Government (0.41)
Leveraging Flexible Digital Approach to Enhance Wellbore Survey Data Management and Collision Avoidance in Offshore Fields, Abu Dhabi
Ness, K. (ADNOC Offshore, Abu Dhabi, UAE) | Kolakkodan, S. (ADNOC Offshore, Abu Dhabi, UAE) | Toader, L. (ADNOC Offshore, Abu Dhabi, UAE) | Zamin, S. A. (ADNOC Offshore, Abu Dhabi, UAE) | Mahmoud, K. Z. (ADNOC Offshore, Abu Dhabi, UAE) | Rabis, P. J. M. G. (ADNOC Offshore, Abu Dhabi, UAE) | Al Ameri, S. M. (ADNOC Offshore, Abu Dhabi, UAE) | Al Marzooqi, A. A. (ADNOC Offshore, Abu Dhabi, UAE)
Abstract This paper aim to identify potential for improving the process of gathering trajectory directional survey data, recalculating of current wellbore position based on new information by automating the process, performing collision avoidance analysis scanning and providing feedback by using machine recognition of risk while minimizing human interaction with the dataset. The envisioned result was seen as a system where wellbore survey data would flow seamlessly from acquisition at rig site into company directional survey calculation system, where programming would use the dataset to update the definitive survey listing, update forward planned surveys, run collision avoidance scan on updated planned surveys against identified offset wellbores and verify current position in relation to plan and possible deviation to same based on company policies for survey and collision avoidance and produce output for end user(s). The project outcome was a software that acts as an intermediate between field data set repository and company directional survey software package. When data set becomes available in repository, a 30-second interval repeating query recognizes the change and updated directional survey data is moved to correct wellbore, used in defining trajectory and original plan is modified allowing collision avoidance verification to run based on new wellbore survey information. The project outcome also included machine review of the collision avoidance results based on programmed company policy values, which added to the process. The project saw substantial time delay during creation due to issues identified in the challenges part below but is now running full time for company covering all offshore rigs and associated wellbore surveying activities. Learnings during the execution of the project showed both short comings of current systems, inconsistent API (Application Programming Interface) support for legacy software, and several opportunities for further improvements to the originally identified goal and potential for creating an advisory system based on current policies, further reducing the sometimes-arbitrary decision making which can result from large variation in experience levels related to understanding risk associated with directional surveying and directional drilling.
- North America > United States (0.94)
- Asia > Middle East > UAE > Abu Dhabi Emirate > Abu Dhabi (0.51)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (1.00)
- Geophysics > Borehole Geophysics (1.00)
Abstract Managed Pressure Drilling (MPD) is a method of controlling wellbore pressure using specialized equipment and techniques that adjust bottom hole pressure in real time. It can be performed with automated or semi-automated systems and trained field personnel, including operators or rig crew. When MPD systems are integrated into drilling rig operations, benefits of MPD systems and processes can be achieved but can be complex. This includes installing equipment, training personnel, and creating policies and procedures. Integrating MPD into a drilling rig has always been challenging due to the timing of rig in and out operations, diversity in MPD equipment, and required of training rig crew to operate MPD equipment or having MPD personnel on site. Automated MPD has also been difficult to integrate and enable the rig crew to run due to the complexity of managing the MPD software. Drilling contractors will continue to be reluctant to carve out additional bandwidth requirements of their rig personnel to deliver MPD in addition to their main scope, as leads to task saturation and creates safety and operational concerns. Ultimately, the term when there is a requirement or expectation of additional or extra labor to operate any equipment is "mechanized" and referring to such a process as "automated" is incorrect. Effective rig integration is crucial for the MPD industry to achieve performance drilling with a proactive MPD approach. Developed under the guidance of experienced super spec drilling rig contractors, The Pressure Management Device (PMD), offers a level of MPD and rig integration not yet experienced by industry while addressing the historical drawbacks, such as manual or mechanized controls, task saturation or additional time & labor burdens for rig crews or the need for an MPD subject matter expert (SME) on site. The PMDโข combines all MPD methods into a single device, eliminating the need for repeated rig in and rig out operations while providing autonomous pressure control without requiring any additional personnel on-site after initial rig up. Incorporating Internet of Things (IoT) and artificial intelligence (AI) technologies, the PMDโข continuously monitors and adjusts pressure conditions in real time. Recognizing that many MPD failures are a result of operator (human) error, this advanced automation allows for seamless integration into existing rig operations which enables the use of MPD techniques without any additional burden on rig personnel or the need for specialized expertise to be on-site. PMDโข additionally provides a significantly reduced equipment complement and footprint that results in more cost-effective transportation, reduced HSE exposure and greater operational efficiencies. Overall, the PMDโข represents multiple major advancements in MPD rig integration offering an effective and efficient solution for drilling operations. With over 90% of traditional piping being eliminated, PMDโข additionally provides a significantly reduced equipment requirements enabling fast and safe handling. In pad drilling applications, and many other scenarios, there is no need to rig out the PMD during well-to-well walks or rig moves. This paper compares traditional MPD to PMDโข performance from a rig integration perspective, focusing on the drawbacks of traditional MPD and how PMDโข addresses them with its patented new technologies, including AI and ML-based software automation and advanced automated systems in a redesigned version of next-generation MPD equipment. It serves as a comprehensive guide for future MPD land and offshore rig integration and outlines the steps to optimize performance with MPD techniques.
Abstract This paper illustrates the methodology and the challenges faced from the planning to execution phases while implementing digital solutions to overcome the drilling operational challenges. In a candidate well, the package with real-time downhole performance measurement (RT-DPM) software, an automated rheometer, and an automatic data graphic visualization interphase, provided visibility into downhole conditions. This was used to predict potential problems and reduce the likelihood of the common issues related to the drilling operation. The RT-DPM software was successfully implemented in a well to reduce the likelihood of stuck pipe incidents and hole cleaning issues. The implementation has enabled real-time monitoring of annular pressure, equivalent circulating density (ECD), equivalent static density, pipe eccentricity, swab, and surge pressure, allowing optimization of the operation time. The lateral section has been drilled successfully with high overbalance without any operational issues. While drilling the production section with several operational challenges, such as losses/gains environment, and high overbalanced formation with a high probability of potential differential stuck, the well was completed successfully, maintaining a good hole cleaning at any point in the annular space of a well. The visibility of the downhole parameters enhanced the rate of penetration (ROP) and optimized the drilling time. A wiper trip was eliminated due to the excellent hole cleaning and the minimal cutting bed generated. Planning started taking into consideration the key point, which was identified as: the close contact points of the pipe to take the extra measurements to avoid such differential sticking in a high overbalanced formation. The overall results were exceptional from the broomstick, showing the parameters were following the ideal trend with no indications of any tight spots. With a steady pick-up weight, slack-off weight, and break-over torque, the hole was identified to be in very good condition. The oil and gas industry is moving to the automation and machine learning methods, and in this paper we will be presenting the methodology and the challenges faced from the planning to execution phases, while implementing automated digital solutions to overcome the drilling operational challenges.
Improving Accessibility of Technical Drilling Applications via Wells on Cloud-based Platform
Mak, W. J. (PETRONAS Carigali Sdn Bhd, Kuala Lumpur, Malaysia) | Aziz, M. L. A. (PETRONAS Carigali Sdn Bhd, Kuala Lumpur, Malaysia) | Hamid, M. R. (PETRONAS Carigali Sdn Bhd, Kuala Lumpur, Malaysia) | Hashim, M. M. H. Meor (PETRONAS Carigali Sdn Bhd, Kuala Lumpur, Malaysia)
Abstract Technical engineering applications have long been an important tool for engineers in the well design process. The nature of being on-prem in individual devices has posed several issues, where lengthy installation processes often lead to a large amount of time consumption and disrupts the engineersโ work efficiency. As the industry moves into the era of digitalization, the Wells on Cloud solution is introduced. The migration of current on-prem applications into a SaaS Cloud-based solution aims to mitigate the issues encountered by users via the elimination of installation processes and automatic push updates. The WoC solution aligns with Wells Digital Roadmap to act as a central hub and a single source of truth while focusing on three key principles. It aspires to cater to solutions from different vendors, capitalize on established Cloud-based infrastructure to host the solution platform and facilitate data transparency. Being a Cloud-based solution, all updates can be implemented and pushed to the user's devices, and users can access their data with a device anytime and anywhere. Direct links to the Open-End Community are incorporated into the solution. Moving on, the WoC's security system consists of active detection of anomalies, protecting against these issues, and comprehensive strategies to respond to any potential breakdown. To measure the value creation resulting from this initiative, the quantitative process cycle efficiency and qualitative feedback survey are conducted. PCE improvements demonstrated the timeline improvement brought by this solution, along with the positive comments shared by heavy users of these applications. The successful deployment of the Wells on Cloud solution has equipped engineers with the tools to unlock the full capability of technical engineering applications right at their fingertips. It also opened the avenues for business agility and scalability to match business requirements, reducing the Total Cost of Ownership and leading to savings. This solution sets the path for Cloud-based working and drives the industry to move towards digitally enabled businesses.
- Information Technology > Security & Privacy (1.00)
- Information Technology > Cloud Computing (1.00)
- Information Technology > Architecture > Real Time Systems (0.46)
- Information Technology > Communications > Web (0.35)
Drilling in the Digital Age: Case Studies of Field Testing a Real-Time ROP Optimization System Using Machine Learning
Al-Riyami, N. (Exebenus, Stavanger, Norway) | Revheim, O. (Exebenus, Stavanger, Norway) | Robinson, T. S. (Exebenus, Stavanger, Norway) | Batruny, P. (PETRONAS Carigali, Kuala Lumpur, Malaysia) | Meor Hakeem, M. H. (PETRONAS Carigali, Kuala Lumpur, Malaysia) | Tze Ping, G. (Faazmiar Technology Sdn Bhd, Kuala Lumpur, Malaysia)
Abstract O&G operators seek to reduce CAPEX by reducing unit development costs. In drilling operations this is achieved by reducing flat time and bit-on-bottom time. For the last five years, we have leveraged data generated by drilling operations and machine learning advancements in drilling operations. This work is focused on field test results using a real-time global Rate of Penetration (ROP) optimization solution, reducing lost time from sub-optimal ROPs. These tests were conducted on offshore drilling operations in West Africa and Malaysia, where live recommendations provided by the optimization software were implemented by the rig crews in order to test real-world efficacy for improving ROP. The test wells included near-vertical and highly deviated sections, as well as various formations, including claystones, sandstones, limestones and siltstones. The optimization system consisted of a model for estimating ROP, and an optimizer algorithm for generating drilling parameter values that maximize expected ROP, subject to constraints. The ROP estimation model was a deep neural network, using only surface parameters as inputs, and designed to maximize generalizability to new wells. The model was used out-of-the-box, with no specific retraining for the field testing. During field-tests, increased average ROP was observed after following recommendations provided by the optimizer. Compared to offset wells, higher average ROP values were recorded. Furthermore, drilling was completed ahead of plan in both cases. In the Malaysian test well, following the software's advice yielded an increase in ROP from 10.4 to 31 m/h over a 136 m drilling interval. In the West Africa well, total depth was reached โผ24 days ahead of plan, and โผ2.4 days ahead of the expected technical limit. Importantly, the optimization system provided value in operations where auto-driller technologies were used. This work showcases field-test results and lessons learnt from using machine learning to optimize ROP in drilling operations. The final plug-and-play model improves cycle efficiency by eliminating model training before each well and allows instantaneous, real-time intervention. This deployable model is suitable to be utilized anytime, anywhere, with retraining being optional. As a result, minimizing the invisible lost time from sub-optimal ROP and reducing costs associated with on-bottom drilling for any well complexity and in any location is now part of the standard real-time operation solutions. This deployment of technology shows how further optimization of drilling time and reduction in well cost is achievable through utilization of real time data and machine learning.
- Asia (1.00)
- Africa (1.00)
- North America > United States > Texas (0.29)
- Asia > Malaysia (0.89)
- Africa > West Africa (0.89)
Abstract Aiming to make the well planning process leaner and agile focusing on duration reduction without compromising quality of deliverables, automation opportunities have been identified within the multi-discipline iterations. The two key criteria considered for the selection of the automation project were: Minimum deployment effort and Maximum value added in efficiency. The initial project objective was to calculate formation tops for a well engineer without requiring the intervention of a geoscientist using commercial software. The methodology utilized is the following: 1. Inputs: Well trajectory and Surfaces. 2. Process: The algorithm finds intersections between surfaces and well trajectory. Surfaces and trajectory are represented as a set of XYZ points. To find the intersection, the software iterates through each point of the trajectory from the top, comparing the depth of the projection to the target surface. The projected depth to the surface is found by 2D interpolation of the surface. Once the trajectory point becomes deeper than the surface projection, the intersection is estimated using geometrical considerations of similar triangles. 3. Deliverables: Estimated formation tops for the given trajectory. 4. Results: Simple in-house developed software enhanced well planning workflow in an Offshore Green Field. The software converted to single executable file and can be run on any device without the open-source software installed. Very accurate results achieved with proposed algorithm with a negligible difference of 0.5 feet with the geoscience traditional software. Well planning duration reduced from average 1 week to 1 or 2 days. The workload for well engineers and the asset team has been dramatically reduced. Reduction of the number of commercial geoscience software licenses required. Way forward: A test with a slightly modified code was used to generate formation tops for more than 400 well in a Long-Term Field Development Plan project for a Brown Field during feasibility study. Upscale to all the Fields within the organization. Improve User Interface for better adoption. Include more formats for both, trajectories, and surfaces. Reduce computing time. This project represents the first initiative in the organization aiming to automate the well planning process. Overall, it represents the beginning of a journey where multiple opportunities for automation can be achieved using an open-source coding software that allows any engineer with little to no experience coding to being able to generate solutions to address daily challenges.
- North America > United States (0.56)
- Asia > Middle East > UAE (0.47)
Unlocking Value From Data is Key to Successful Digital Transformation
AlSaadi, Hamdan (ADNOC Offshore) | Rashid, Faisal (ADNOC Offshore) | Bimastianto, Paulinus (ADNOC Offshore) | Khambete, Shreepad (ADNOC Offshore) | Toader, Lucian (ADNOC Offshore) | Landaeta Rivas, Fernando (ADNOC Offshore) | Couzigou, Erwan (ADNOC Offshore) | Al-Marzouqi, Adel (ADNOC Offshore) | El-Masri, Hassan (Schlumberger) | Pausin, Wiliem (Schlumberger)
Abstract Big data analytics is the often complex process of examining large andvaried data sets to uncover information. The aim of this paper is to describe how Real TimeOperation Center structuring drilling data in an informative and systematic manner throughdigital solution that can help organizations make informed business decisions and leverage business value to deliver wells efficiently and effectively. Real Time Operation Center process of collecting largechunks of structured/unstructured data, segregating and analyzing it and discovering thepatterns and other useful business insights from it. The methods were based on structuringa detailed workflow, RACI, quality check list for every single process of the provision of real-timedrilling data and digitally transform into valuable information through robust auditableprocess, quality standards and sophisticated software. The paper will explain RTOC DataManagement System and how it helped the organization determining which data is relevantand can be analyzed to drive better business decisions in the future. The big data platform, in-house built-in software, andautomated dashboards have helped the company build the links between different assets,analyzing technical gaps, creating opportunities and moving away from manual data entry(e.g. Excel) which was causing data errors, disconnection between information and wastedworker hours due to inefficiency. These solutions leverage analytics and unlock the valuefrom data to enhance operational efficiency, drive performance and maximize profitability. As a result, the company has successfully delivered 160 wells in 2019 (6% higher than 2019 Business Plan and 10% higher than number of delivered wellsin 2018) more efficiently with 28.2 days per 10kft fornew wells (10% better than 2018), without compromising the well objectives and quality of the wells. Moreover, despite increasing complexity, the highest level ofconfidence on data analytics has permitted the company to go beyond their normaloperating envelop and set a major record for drilling the world's fifth longest well as amilestone in 2019.
- Information Technology > Data Science > Data Quality (1.00)
- Information Technology > Data Science > Data Mining > Big Data (1.00)
- Information Technology > Architecture > Real Time Systems (1.00)