Second EAGE Workshop on Pore Pressure Prediction

Pore pressure prediction has been a vital concern to the oil and gas industry for many decades. Most petroleum provinces exhibit overpressures, and as the industry explores for deeper targets, encountering substantial overpressures are becoming more common. As deeper, hotter and more complex geology is drilled, the technical difficulties in predicting pressure, designing a well and safely drilling through complex overpressures. This specially caused by multiple mechanisms in “wildcat” settings have become extremely challenging and require innovative technique to be utilized. Several of the recentiy drilled wells encountered strongly over-pressured intervals which caused operational challenges and required finetuning of the pre-drill models based on the real time data.

Estimation of pore and fracture pressures in complex tectonic settings, such as fold-and-thrust belts and their adjacent foreland basins, is usually challenging and often requires local calibration. The Cenozoic North Alpine Thrust Front and Foreland Basin in SE Germany provide an interesting and challenging example for such a complex system, comprising overpressured sections in the thrust belt, the overthrusted foreland sediments below the main detachment and the undeformed foreland basin. Based on the synthesis of recent research in the study area and new analyses of additional well data from the thrust front, pore and fracture pressure models will be presented for the different parts of the North Alpine Thrust Front and Foreland Basin in SE Germany. The results demonstrate, that understanding of the geological history, stress regime variations and overpressure generation are key to estimate pore and fracture pressures in complex tectonic settings, such as the North Alpine Thrust Front and Foreland Basin in SE Germany.

We evaluate the FES pressure prediction workflow using results from an evolutionary transient geomechanical model. The FES workflow couples velocities with geomechanical modeling to incorporate the effects of both mean and shear stress to pressure generation. The FES method predicts pore pressure and the full stress tensor. Because the FES workflow is iterative and requires data available on different grids (e.g., velocity vs. geomechanical results), we have developed a new tool in Horizon/Elfen to streamline the prediction process. In order to evaluate the workflow, we consider the end stage of the evolutionary model as the real basin. We use the geometry to build a static model. We use the evolutionary porosity field to calculate our real velocity field. We apply the VES method using this velocity field and the FES method using the velocity field and the static model. We find that the FES method predicts pressure values closer to the real basin pressures and performs better near a source-layer weld, where both mean and shear are non-uniaxial.

In unconventional plays, given the comparatively short drilling times and the likelihood that operators have multiple active rigs, wells are drilled and data are acquired at an unprecedented rate whereby a new well is completed every 1–2 days at a cost of $6–9M per well. Therefore, performing manual workflows for petrophysics, pore pressure and geomechanics prediction can be impractical due to turnaround considerations and the multiple personnel required. This, together with technical challenges of complex stratigraphy, multiple facies, variable rock properties, and the interaction of pore pressure and geomechanics, calls for more consistent, sophisticated, and faster analytical tools. A supervised deep neural network approach is presented as an innovative tool to devise solutions which simultaneously integrate myriad data types. Furthermore, an algorithm was developed to predict a certain number of attributes solely from a facies-based seismic inversion, namely Vp, Vs, and Rho. The application of these algorithms on various blind wells from a Permian Basin case study, both within and outside the seismic survey, shows a reasonable accuracy when compared to manually interpreted counterparts but were obtained in a fraction of the time, hence, provide a promising outlook for the application of deep learning in integrated studies.

The rhob-velocity-effective stress (RhoVe) method represents an empirical approach to pore pressure analysis and calibration that utilizes a series of model-driven, genetically-linked “virtual” rock property relationships. The method is fundamentally a two-parameter approach (a-term and α), that is used to construct a velocity-vertical effective stress (VES) and density-VES family of curves that can be applied to a well of interest where convergence of the two transformed properties offers a robust solution. Once calibrated, the construct represents a fully-populated” petrophysical (shale-only) model volume that can be queried and interrogated to perform advanced calculations leading to a new empirical approach for calculating pore pressure from temperature that both frames the structural-stratigraphic history of fine-grained clastics in a sub-regional setting and allows for an interpretation of local diagenetic effects. The method utilizes a single master power law relationship between temperature and α’ that is applied as an instantaneous series. The same temperature — α’ power law function transforms sonic and density data for the entire stratigraphic section. Accounting for the effects of ongoing chemical compaction and diagenesis using alternate associations like temperature extends the predictability of high-velocity, high-density, low-effective