The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
- Data Science & Engineering Analytics
The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
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Data Science and Engineering Analytics News All the Ways Bad Data Is Holding You Back Energy Industry Apps Improve Efficiency SPE Energy Stream SPE Energy Stream is your go-to for watching thought leaders, subject-matter experts, and leading companies share their perspectives and technical solutions. View more on SPE Energy Stream Online Education Join industry experts as they explore solutions to real problems and discuss trending topics in Data Science and Engineering Analytics. SPE webinars are free to members courtesy of the SPE Foundation. Online Journal Data Science and Digital Engineering is SPE's newest online publication, presenting the evolving landscape of data management and use in the industry with original content from SPE and content curated from other relevant publications. Featured Title Data-Driven Reservoir Modeling Shahab D. Mohaghegh Data-Driven Reservoir Modeling introduces new technology and protocols (intelligent systems) that teach the reader how to apply data analytics to solve real-world, reservoir engineering problems.
SPE's Paige McCown talks with John de Wardt, founder of De Wardt and Company and program manager of the Drilling System Automation Roadmap to discuss the need for an industry roadmap. In this podcast discussion, we talk about how artificial intelligence can help the industry through the digital transformation. This transition is a priority for oil and gas companies to reduce risk while improving operational efficiency. In this podcast discussion, we are talking about the way ahead for students and recent graduates in petroleum engineering. The COVID-19 pandemic-induced oil and gas downturn has caused a lot of uncertainty for students, especially petroleum engineers.
Mark Rubin is responsible for overall management of SPE. He works with the Board of Directors and senior staff to develop strategic and business plans and formulate the organization's goals, objectives, policies, and programs. He also organizes and directs the global staff organization, which includes offices in Dallas, Houston, Calgary, Kuala Lumpur, Dubai, and London, to ensure the accomplishment of SPE's mission. He was appointed executive director of SPE in August 2001. Prior to this appointment, he served as upstream general manager for the American Petroleum Institute (API) in Washington, D.C.
Ipieca has published its free online labor rights training. Developed with the contribution of Building Responsibly and hosted on the Supply Chain Sustainability School's e-learning platform, the training consists of 12 modules which aim to improve awareness of labor rights issues among those responsible for working conditions and labor practices, so that they can better recognize problems and address them appropriately. The training includes modules on child labor, fair recruitment, wages, worker representation and engagement, working hours and overtime, nondiscrimination and equal opportunities, worker accommodation, procurement, worker grievance mechanisms, and forced labor. Each module includes introductory text, videos, case studies, a one-pager of key lessons, and quiz questions to test understanding. Links to relevant additional resources and Ipieca guidance are also included in the modules.
For oil companies whose job is converting barrels to dollars, the enemy is downtime. Downtime can result in lost barrels, and thus lost revenue. Unscheduled downtime is the worst offender. When something goes wrong, and there was no hint that trouble was coming, it can take production offline for longer than otherwise expected. Keeping operations firing on all cylinders is important for both the health of the field and the producer's bottom line.
Abstract BP has had a presence in Oman since 2007 and stands as a major investor within the country. BP is one of the world's pioneers in tight gas production, harnessing technology and experience to develop one of the Middle East's largest unconventional gas resources in the Sultanate's Block 61. BP Oman's overall goal is to create a sustainable legacy that supports Oman's strategic goals for energy security and long-term economic diversification. Production from Phase 1 of Block 61, Khazzan, started in 2017 (Fig. 1). In October 2020, production from Phase 2, Ghazeer also started (Fig. 2). Combined, Khazzan and Ghazeer produce 1.5 billion cubic ft of gas/d and more than 65,000 bbl/d of associated condensate. With an estimated 10.5 trillion cubic ft of recoverable gas resources, the block has the capacity to deliver approximately 35% of Oman's total gas demand.
Abstract The oil and gas (O&G) business is going through a natural but accelerated evolution to incorporate more digital tools into its business model. While it is important to adapt quickly, under such circumstances it is equally important to plan with the future in mind. The biggest challenge the oil and gas industry has faced recently is the sudden shifts in demand and supply. The pandemic era curbed demand, while the recovery resulted in an unprecedented supply shortage. This imbalance identified an opportunity to quickly evolve new sales and operational planning (S&OP) processes. These processes were designed to continually balance supply and demand at different time horizons. The digital capabilities were further empowered to harness S&OP potential to forecast and plan better than ever before. The digital planning tool (DPT) is now part of the business fabric, enabling product forecasting up to 12 months out. This paper illustrates one of the steps in the technological digital journey driven by O&G service companies. It shows how implementing a DPT enables S&OP processes to optimize resource management. Such optimization creates a more agile and accurate response to a fluctuating demand-supply balance, improving company performance overall.
Amorocho, A. (Drilling Technology ADNOC, Abu Dhabi, UAE) | Elkasrawi, A. (Drilling Drilling Materials ADNOC, Abu Dhabi, UAE) | Abdelazim, A. (Drilling Drilling Materials ADNOC, Abu Dhabi, UAE) | AlRashdi, A. (Drilling Drilling Materials ADNOC, Abu Dhabi, UAE) | Shamlam, A. Bin (Drilling Operations ASR/UC/ASAB ADNOC, Abu Dhabi, UAE) | Nuaimi, M. Al (Drilling Technology ADNOC, Abu Dhabi, UAE) | Nunez, Y. (Drilling Technology ADNOC, Abu Dhabi, UAE) | Blanpied, C. (Middle East Services Director – Vallourec, Abu Dhabi, UAE) | Cavanha, T. (Business Owner OCTG Digital Solutions – Vallourec, Paris, France) | Blues, S. (Vallurec ME, Vallourec, Abu Dhabi, UAE)
Abstract Responding to requirements of Operator Company in Abu Dhabi to automate and strengthen processes of running casing and tubing, a patented digital solution has been implemented, which timestamps all key phases of the tubulars’ lifecycle from rig receipt to running then to rig return, while enabling continuous improvement through post-running data analytics. The solution relies on unique individual pipe traceability, through a combination of different methods of marking such as – data matrix, RFID & barcodes. These markings are read using a variety of digital tools including – smartphones, tablets & cameras. The solution has already been deployed in North & South America, Europe, and Asia, totaling over 100 successful jobs worldwide. Operator Company in Abu Dhabi was the first operator in the Middle East to try the solution in 2022. The below section summarizes the solution results based on the feedback from the first three wells piloted by Abu Dhabi Oil Company. The value chain is broken down into three key categories as follows: –Pre-running: the solution brought an increased level of quality control paired with an automatically generated pre-tally list. Further to this, an increase in personnel is safety assisted by a reduction in tubular handling and removal of personnel from high-risk positions. –During running: the accuracy of the running sequence was ensured by the utilization of the solution ‘‘Watchdog Alerts’. These highlighted to the user any deviation from the original plan, preventing error and minimizing any downtime generated. All of this was made available in real-time in a cloud environment to anyone within the Operator Company with credentials for accessing the system. –Post-running: monitor and compare rig performances through digitally enabled data analytics In conclusion, significant cost reduction (from 15 to 45 k$ per job for a 70k$ rig day rate), mitigating risks of non-productive time by reducing human errors (from 5 to 15 hours per job), ensuring safety and integrity of the well and enabling operators to track its assets and monitor running operations in real-time.
Bhawna, Ahuja (Halliburton, Bangalore, Karnataka, India) | Gurunath, Gandikota (Halliburton, Bangalore, Karnataka, India) | Shashwat, Verma (Halliburton, Bangalore, Karnataka, India) | Yogesh, Sharma (Halliburton, Bangalore, Karnataka, India)
Abstract The daily drilling report (DDR) contains information on daily activities and parameters from the well operations. The inputs are classified using activity codes to evaluate the field performance with improved decision-making. The coding levels support hierarchy in activity code sets. However, it requires information about a substantial number of codes and subcodes. Thus, accurate and consistent identification of codes for operation activities becomes challenging and time-consuming. This work proposes a novel approach to automatically suggest the activity code for drilling activities in well information management system (IMS), with the aim of facilitating the digitization of well operations. We propose a natural language processing (NLP) based two-stage machine learning (ML) model for prediction of activity codes using drilling activities descriptions. The methodology consists of data analysis to identify critical factors for developing ML model. To handle challenges of the diversity of the larger dataset, sampling approach is adopted. Augmentation via contextual embeddings is also explored for minority class. The term frequency-inverse document frequency (TFIDF) is used for feature extraction from text. The classifier is first trained to predict the main activity codes. Predicted main codes in the first stage become the feature space for the second stage training for enhanced accuracy. To improve the accuracy further, related subcodes are grouped according to confusion matrix, performance, and expert advice. This ML model is then integrated with IMS. This method was implemented on a large dataset consisting of 3000+ wells with 1M+ rows. With 70% of the dataset for the training, accuracies achieved for subcode prediction include 66% for the conventional model, 83% for grouped subcode prediction, and 92% for the proposed two-stage grouped subcode prediction. Hence, the proposed model outperforms the conventional model significantly. It is observed that the number of codes/subcodes affects the accuracy. During microservice development, memory requirement and latency are also examined. Increasing tree depths of the ML model after a certain point does not offer significant accuracy improvement though it leads to greater memory requirement and latency. Compression reduces the memory requirement significantly but at increased latency. Hence, an optimal trade-off between accuracy, latency and memory requirement may be attained by selecting model features. It is, therefore, established that the proposed workflow can be used to assist the digitalization of activity code mapping with potential benefits of improving performance, efficiency and reduced manual efforts in database information system for improving efficiency. Novelty of this approach lies in the use of two stage prediction where hierarchical nature of codes is utilized for enhancing accuracy with the help of advanced technologies such as NLP and ML. Grouping of related codes with expert knowledge and performance also provides a realistic solution for reducing the manual efforts.
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 ∼24 days ahead of plan, and ∼2.4 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.