A useful glossary of common industry terms from a global provider of knowledge management solutions
AVO, or Amplitude Variation with Offset is the observed variation of a given seismic reflector’s amplitude as a function of offset or angle. Seismic reflectors occur where contrasts in elastic properties are present in the subsurface. The AVO interpretation can help infer something about those elastic properties above and below the interface. Rock physics allows us to link the elastic properties to the geological and petrophysical properties we are interested in.
Digital transformation in oil & gas means adopting new industry technologies to allow scientists and engineers to make better decisions and add additional value to the organization. This could be done in many play types, including conventional exploration.
Geomechanics encompasses rock mechanics, structural geology, and petroleum engineering to address a wide range of problems in the subsurface when drilling and producing oil and gas.
The Open Subsurface Data Universe (OSDU) is an industry-driven initiative to encourage data sharing between applications and a new breed of cloud-native software platforms that promise to allow users to work in virtual teams, seamlessly sharing information and insights.
Pore pressure prediction is a science that involves modeling the subsurface to understand what pressures we should expect to encounter. This prediction can improve drilling success and safety.
Quantitative Interpretation (QI) aims to improve our understanding of the subsurface by making quantitative predictions of subsurface properties and geometry from seismic data. QI is based on rock physics, which links the properties we are interested in (rock type, porosity, fluid saturation, etc.) and those the seismic data responds to. QI is, therefore, ultimately the prediction of geological and petrophysical properties from seismic data.
Real-time pore pressure monitoring is the ability to monitor downhole conditions to update the pore pressure and fracture gradients to help reduce uncertainties, events, and drilling risks.
Rock physics is creating models to understand the link between the geological and petrophysical properties and the observed seismic response. This helps to gain insight into what is driving the seismic responses observed between wells.
Seismic inversion is converting seismic amplitudes into estimates of elastic properties and, depending on the technique used, elastic facies. Seismic inversion is a critical step in the seismic reservoir characterization workflow.
Seismic reservoir characterization is a technique used to enhance our understanding of subsurface architecture and petrophysical properties based on seismic data.
Simulation-to-Seismic or sim2seis workflow transforms dynamic reservoir simulation data into synthetic seismic models based on rock physics. This allows assessment of the 4D seismic reservoir monitoring technique as a potential tool for reservoir delivery and provides a mechanism for a ‘history match’ of the reservoir simulation between wells.
Subsurface data integration eliminates data silos and bottlenecks while seamlessly connecting your digital ecosystem of applications, databases, and software technology.
Subsurface data management involves the aggregation, administration, and distribution of subsurface data to enable access and utilization of data for improved understanding and decision-making.
Wellbore stability is preventing the surrounding wellbore rocks from plastic distortion or brittle collapsing because of mechanical stress. Proven methods, such as burial, stress, temperature histories, rock type, distribution, and subsurface structure, maintain a wellbore’s stability.
A well-tie is the process of matching log responses to seismic reflectors at the well location. This is a fundamental step when interpreting seismic data. A vital part of the well-tie process is the estimation of the seismic wavelet. A proper well-tie and wavelet estimation process should allow a quantitative analysis of the seismic phase, frequency, and other attributes and an understanding of accuracy and goodness-of-fit. Good well-ties are essential for quantitative seismic interpretation workflows.
