A major operator providing oil and gas exploration and extraction services in the Caspian Sea region deployed an ensemble simulation workflow utilizing the Aspen Big Loop technology to perform uncertainty and risk estimation as part of the project feasibility assessment, with Aspen Tempest as an orchestrator.

Field development planning using reservoir models is a key step in the field development process. Numerical optimization of specific field development strategies is often used to aid planning, and Bayesian Optimization is a popular optimization method that has been applied in the past.

The Reykjanes reservoir, lying in a volcanic environment in Iceland, is assumed to contain supercritical fluids, whose energy content is about ten times higher than conventional geothermal systems. Drilling a well in this environment has to cope with very high temperature and pressure as well as an intensely fractured subsurface. A detailed well design can help minimize the likelihood of drilling and completion problems that could compromise the success of the project.

Geothermal energy is a key resource for providing clean, reliable and sustainable energy. Induced seismicity events, with magnitudes large enough to be felt by local communities and impact surface infrastructures, are an undesirable potential result of geothermal operations, and significantly impact social acceptance. Mitigating adverse effects is of upmost importance if one wishes to increase the number of safe, sustainable geothermal projects.

Seismic data can provide useful information for prospect identification and reservoir characterization. Combining seismic attributes helps identify different patterns, for improved geological characterization. Machine learning applied to seismic interpretation is very useful in assisting with data classification limitations.

In depth imaging, depth calibration using well-tie updated velocity is generally applied to poststack data. This is the most correct type of depth calibration as it positions structures in their proper places.

One of the main challenges in seismic imaging, especially of land data, is building the near surface velocity model, as it is normally characterized by very low velocity values with different types of local anomalies. Resolving the near surface velocity model using only refraction data is insufficient, as it does not provide the required lateral resolution. Using only reflection data is equally insufficient, as it does not provide the required vertical resolution.

The concept of digital twins has been around since 1960s, but the term was first introduced nearly a decade ago by NASA's John Vickers. Until very recently, adoption of digital twins has been inconsistent, but acceptance looks to be accelerating with recent market shifts. Today’s users are seeing exponential benefits by deploying multiple digital twins across sites. The next wave of digitalization innovations, powered by digital twins and AI, is creating new levels of agility and flexibility, paving the way to autonomous operations.

Interested in learning how digital solutions support end-to-end gas exploration and production to meet the increased demand for natural gas? Watch AspenTech’s Subsurface Science & Engineering experts discuss how you can optimize operations across your assets’ lifecycle to offset threats of sustainability and energy security.

Meet our experts at the leading multidisciplinary geoscience and engineering event in Europe, and learn how AspenTech® Subsurface Science & Engineering is helping companies like yours. The show will focus on energy security and independence, a sustainable energy transition, and how we all need to adapt our traditional ways of working and cooperation to address an uncertain future.