Leveraging Large Subsurface Data and Associated Uncertainties to Build High Quality 3D Structural Model

Abstract

At brownfield development stage, dealing with diverse and large amount of data makes it challenging to integrate them all in a consistent manner to build a prime structural model. Like many others, the studied field consists of several-stacked reservoirs featuring many faults and close to a thousand drilled wells with vertical, slanted and horizontal trajectories. On top of that, many horizontal wells are targeting thin carbonate layers for which tightly spaced data points often result in conflicting observations. Consequently, horizontal and deviated wells are commonly discarded from structural modelling, leaving substantial and valuable information unused. Some of these wells may be indirectly accounted through the introduction of pseudo-wells, making the modelling workflow tedious, user-dependent and therefore difficult to repeat. ’It's better to be approximately right than exactly wrong’ quoted by Carveth Read, 18th century. Accordingly, every physical measurement, even from the most modern and sophisticated tools, is subject to some uncertainty. Therefore, assessing the uncertainty related to each input data is paramount in this method. Integrated teamwork between geologists, geophysicists and drilling specialists lead to a thorough analysis of each data feeding the process of structural model building while providing best uncertainty estimates. The ranges were specified for ∼1000 well trajectories, ∼16000 geological markers, 3 seismic travel time maps, 3 interval velocities and 59 thickness maps. All available data are used in a consistent manner to minimize the depth uncertainty. The accuracy is further improved by linking together all surfaces in a multi-layered model. In addition, this methodology considers both large scale spatial continuity capturing structural trends and more local scale incorporating inter-well variations of thickness due to sedimentological controls. After following this approach, all subsurface data started to come in agreement and resulted in more geological architectures. As an example, Figure 1 shows a cross-section along a well that drilled in B4 target layer which average thickness of 6 ft. As illustrated in the left figure, classical workflow using vertical wells and some pseudo-wells resulted in an anomalous pull-up structure and overall wavy non-geological geometry. Moreover, the well shows that it is in non-reservoir dense layer even though the well in the reservoir based on the zone log interpretation. However, the right figure shows that considering horizontal wells and uncertainties help to integrate all subsurface data with improved consistency where the structure model is smoother & more geological, plus the well is correctly placed in the targeted reservoir. This approach will make the studied field one of the first brownfields that incorporated all data in consistent manner without pseudo-wells to build 3D structural model. It will bring considerable value to reduce uncertainties during subsequent property and dynamic modelling stages.