Chevron (United States)

Abstract Openhole (OH) toe isolation in deepwater high‑pressure, high‑temperature (HPHT) wells remain operationally complex when deeper drilling is required to confirm reservoir boundaries, oil–water contact (OWC), or proximity to salt. Conventional balanced cement plugs can be time‑consuming, uncertain, and risk additional remediation at ultra‑deep measured depths. This paper documents the testing, integration, and field deployment of a metal expandable packer (MEP) used as an open hole toe isolation plug in a deepwater well at ~30,000 ft true vertical depth (TVD) under HPHT conditions (to ~21,000 psi and ~350°F). The MEP concept leveraged existing inventory, a modified liner‑hanger running tool (RT), and a structured engineering and risk‑management process to enable rapid mobilization and execution. The design review covered metallurgy (Inconel 718 base pipe with Sanicro 28 sleeve and SAF 2507 components), elastomer selection [FFKM (Perfluoroelastomer) / TFE-P (Tetrafluoroethylene-Propylene)], and anchoring/sealing requirements (ISO 14310 V3 seal qualification; axial load capacity up to ~1,000,000 lbf). Laboratory and surface integration tests confirmed the RT could actuate and release at required pressures (MEP expansion ~5,000 psi, hydraulic release ~6,000 psi). Field development for the operator involved drilling and completing three wells. A planned contingency was executed on the second well when an undesired zone was intersected at the base of the well. The MEP OH plug was deployed and set per procedure, enabling immediate progression to the OH gravel‑pack completion. Compared with a balanced cement plug (BCP), the MEP approach reduced operational time from ~80 h to ~52.5 h for toe isolation, translating to a total cost reduction from ~$3.7 MM to ~$2.7 MM for an assumed $1 MM/day rig spread, while avoiding cement contamination risks. Results, best practices, and an economic comparison are presented to support broader adoption of MEPs for open hole toe isolation in deepwater HPHT applications.

Well integrity monitoring, especially annulus pressure surveillance, is essential for detecting abnormalities and maintaining safe production. Modern sensors enable real-time measurement of pressure, temperature, and other parameters in the well and at the wellhead. Even so, human intervention is currently required to interpret data trends, flag issues and make decisions. Machine learning may strengthen this process by automating activity recognition and detecting anomalies in near real time, reducing reliance on manual analysis on research datasets, subject to further validation before any operational use. We compare two machine learning approaches: an Inference System and Long Short-Term Memory (LSTM), by applying them to time series data for production activity recognition and abnormality detection. The dataset, consisting of minute-by-minute measurements of pressure, temperature, and valve statuses from four oil production wells in the Gulf of America, was pre-processed through cleaning and imputation before being used for model training and evaluation. The inference system combines expert knowledge with statistically derived insights to create rules as part of a rule-based method. In contrast, the LSTM model learned from labeled input data. Both approaches first classified production activities into categories such as well shut-in-steady, well shut-in-transient, production-steady, production-ramp-up, production-ramp-down, production-tubing-side or production-annulus-side valve status change, and other well activities. This classification helped better interpret real-time well activities and increase the accuracy of anomaly detection. Pressure and temperature abnormalities primarily detected during steady-state activities, such as well shut-in-steady and production-steady, were used to assess well integrity. Here, the rule-based method relied on statistical analysis to define rules for abnormality detection, while the LSTM model achieved the same task by learning from supervised data. On the evaluated research dataset, results show that the LSTM model outperformed the inference system, achieving an F1 score of 94% for well activity classification and 91% for abnormality detection. These results reflect performance on a limited dataset and do not imply predictive accuracy in field operations. The machine-learning methods and results presented in this paper are research-oriented and do not represent the operator´s engineering standards and operating procedures. The methods detailed in this paper illustrate how automatic well activity recognition may be approached, enhances abnormality detection, supports earlier well integrity issue identification, minimizes dependence on human judgment, and supports root-cause identification of well integrity issues when combined with other techniques. Such automated routines suggest feasibility as future digital tools for a well intervention operation may play a role in future research and system development.

Abstract This study investigates how mechanical and chemical engineering curricula worldwide are adapting to the energy transition by integrating renewable energy, carbon capture and hydrogen technologies, digitalization/AI, and sustainability. A global survey was distributed to 1000+ universities and directed to academic leaders and curriculum committees; 18 early responses were analyzed and benchmarked against a petroleum-engineering study reporting 82% of programs updated in response to energy expansion. Preliminary results indicate adaptation is underway but uneven: 72.2% of programs report partial integration, 22.2% report full integration, and 5.6% report none, with two-thirds of “partial” adopters at ≤25% curriculum coverage. Change occurs within existing structures (no reported department name changes) and spans multiple tiers: 50% cite updates across BSc + MSc + PhD. By level, BSc emphasizes broad exposure, with Sustainability 83.3%, Renewables 77.8%; MSc shows deeper technical expansion, with Sustainability 66.7%, Renewables 66.7%, CCUS/H₂ 66.7%, AI 44.4%; PhD shifts remain selective, with Renewables 38.9%, AI 38.9%, Sustainability 33.3%, with 44.4% reporting no change. These early signals suggest mechanical/chemical programs are directionally aligned with petroleum's transition topics but lag in depth, especially on CCUS/H2 at scale and explicit climate content, highlighting the need for ready-to-adopt materials and cross-level coherence to accelerate reform and better prepare engineers for emerging energy demands.

International audience

Abstract Operational efficiency is critical to the economic viability of deepwater wells. To accelerate completion timelines, the Multistage Single Trip (MSST) system was introduced in the early 2000s, enabling frac-and-pack stimulation across multiple intervals in a single deployment. While this innovation significantly reduced rig time and operational complexity, it necessitated a dedicated deburring run to mitigate the risk of damage to isolation packers caused by perforation burrs. To further optimize the MSST system, metal expandable technology was integrated, eliminating the need for the deburring trip. This paper presents the planning, testing, qualification, deployment, and field results of incorporating the Well Expandable Inner String Packer (WEA-ISP)—comprising the Metal Expandable Packer (MEP) and Well Expandable Anchor (WEA)—into the MSST system. This integration replaced legacy isolation packers and enabled safe passage through perforated intervals without compromising zonal isolation or system integrity. A multidisciplinary collaboration ensured the successful deployment of WEA-ISP without altering well objectives. Key design parameters—including burr height, perforation length, and clearance—were identified to perform rigorous test on the WEA-ISP. The system underwent comprehensive ISO 14310 qualification, demonstrating its ability to withstand up to 15,000 psi differential pressure and anchoring forces exceeding 480,000 lbf. Field deployments in four deepwater wells, including both water injectors and oil producers in the Gulf of America, validated the system's performance. The integration of WEA-ISP into the MSST system eliminated the deburring run, saving an average of 2.7 days of rig time per ~30,000 ft well—equating to over $3 million in cost savings per well. The system supports both 10,000 psi and 15,000 psi treatment pressures without requiring additional inventory. This paper provides detailed insights into qualification protocols, engineering documentation, operational procedures, and contingency planning. The successful implementation of WEA-ISP within MSST completions demonstrates a robust, cost-effective solution that maintains reliable zonal isolation while significantly enhancing operational efficiency.

Gears play a significant role in the field of Mechanical Engineering. They are the pioneers of energy transmission, utilized in force transmission, multiplication, and enhancement. They have been used in various fields, from large human-crewed vehicles, tractors, and trucks to the mechanical wristwatch, the most minute gear applications. Many studies have shown the use of gears and the rate and the cause of their failure, which range from pitting, scoring, fracture, wear, and thermal stresses. This study focuses on the use of organic carburizers to perform Carburization. This study provides a thorough understanding of Carburization by exploring the mechanisms of carbon diffusion, the effects of temperature and time on case depth, and the ensuing microstructural transformations. The project also looks at how Carburization affects mild steel and fatigue life, emphasizing its importance for increasing gear longevity and dependability, which could be recommended to be used in manufacturing spur gears with better wear resistance, strength, and ductility, hence reducing their failure rate.