Aspen Mtell Predictive Maintenance Solution

A comprehensive oil and gas company faced the challenge of unplanned downtime occurring in critical assets such as turbo expanders, gas compressors, and gas turbines. The potential impact of unplanned downtime includes costly repairs and maintenance, along with a daily cost of \$1.2 million due to production delays. Early detection of events is crucial as it allows valuable time to be returned to production and enables the mitigation or resolution of issues through operations. The company implemented AspenTech's prescriptive maintenance solution, Aspen Mtell, to improve asset uptime and reliability. **What Aspen Mtell Can Do** - Provides accurate early warnings of critical asset failures up to 23 days in advance. - Enhances the reliability of critical assets and plant uptime, ensuring uninterrupted operations. - Improves process safety, resulting in reduced environmental impact and associated costs. - Reduces annual maintenance costs across the site by quickly extending solutions to similar assets using Aspen Mtell's transfer learning capabilities. For more details, please refer to the related catalog.

YPF, Argentina's largest oil and gas company with a 100-year history, has begun its transformation efforts in collaboration with AspenTech. As a leader in the regional energy industry, the company has set ambitious goals, including venturing into crude oil and LNG exports and quadrupling its corporate value over the next four years by enhancing the production efficiency of its refineries. [Challenges] - To achieve these goals, it was necessary to significantly reduce operating costs while ensuring the reliability of equipment. [Results Achieved with Aspen Mtell] - Early detection of failures prevented significant damage to critical compressors. - Avoided an estimated 10 days of opportunity loss due to increased vibrations leading to failures, as well as preventing uncontrolled vents that could cause environmental pollution. - The implementation of the system led to an improvement in safety culture, streamlined operations, and promoted proactive problem-solving. For more details, please refer to the related catalog.

Sardeolica operates a 126 MW wind farm consisting of 57 wind turbines located in Urasai and Perdaxius, Italy, with an annual generation of 250 GWh. Since 2017, the company has embarked on a digitalization process aimed at optimizing plant control, planning maintenance during low wind periods, continuous improvement of productivity and machine uptime, and realizing a "digital" maintenance culture. 【Challenges】 • Extending the lifespan of the wind farm (a total of 57 turbines) and reducing maintenance costs • Optimizing scheduling through predictive maintenance requirements 【Achievements with Aspen Mtell】 • Proactively managing the wind farm to avoid catastrophic damage • Predicting potential issues up to six months in advance • Planning maintenance work during low-impact low wind periods • Improving plant uptime while reducing annual maintenance costs by up to 10% For more details, please refer to the related catalog.

OCP Ecuador is a midstream oil and gas company that transports, stores, and ships crude oil. They showcase a case where they have implemented AspenTech's predictive maintenance solution, Aspen Mtell, to continuously monitor critical assets using both process data and mechanical data. 【Value Created by Adopting AspenTech Products】 • Accurately detected imminent asset failures, such as combustion issues and valve and injector calibration problems, 20 days before they occurred. • Rapidly applied the solution to 22 main pumps, 5 booster pumps, and 4 generators. • Generated three times the initial investment in less than five months. For more details, please refer to the related catalog.