Simplified analytical methods are used in 1D geomechanics workflows to predict the rock's behavior during drilling, completion and production operations. These methods are simplistic in their approach and enable us in getting a time-efficient solution, however it does lose out on accuracy. In addition, by simplifying equations, we limit our ability to predict behavior of the borehole wall only i.e. near wellbore solutions. Using 1D analytical methods, we are unable to predict full field behavior in response to drilling and production activities. For example, when developing a field wide drilling plan or preparing a field development plan for a complex subsurface setting, a simplified approach may not be accurate enough and on the contrary, can be quite misleading. A 3D numerical solution on the other hand, honours subsurface features of a field and simulates for their effect on stresses. It generates solutions which are more akin to reality. In this paper, difference between a simplified semi-quantitative well-centric approach (1D) and a full field numerical solution (3D) has been presented and discussed. The subsurface setting considered in this paper is quite complex - high dipping beds with pinch outs and low angled faults in a thrust regime. Wellbore stability and fault stability models have been constructed using well-centric approach and using a full field-wide 3D numerical solution and compared to understand the differences. In this study, it was clearly observed that field-based approach provided us with more accurate estimation of overburden stresses, variation of pore pressure across the field, changes in stress magnitudes and captured its rotation due to pinch-outs and formation dips. For example, due to variation in topography, the well-centric overburden estimates at the toe of deviated well at reservoir level is lower by 0.21gm/cc as compared to the 3D model. It is also observed that within the field itself stress regime changes from normal to strike slip laterally across the reservoir. In comparison to 1D model, considerable differences in stable mud weight window of upto 1.5ppg is observed in wells located close to faults. This is due to effect of fault on stress magnitude and azimuth. Stress state of 4 faults were assessed and all are estimated to be critically stressed with elevated risk of damaging three wells cutting through. However, a simple 1D assessment of stress state of faults at wells cutting through them, show them to be stable. Moreover, the 3D geomechanical properties that are input into the numerical simulation also play an important role on the results. The algorithms and data used to populate the properties away from the well, need to be validated and calibrated with the well data, to predict reliable results. As the subsurface was quite complex, and well data was not sampled optimally, both horizontally and vertically, the selection and optimum usage of 3D trends also became crucial. By comparing the differences between 1D and 3D solutions, importance of 3D numerical modelling over 1D models is highlighted.
Petroleum Geologists typically study hydrocarbon bearing reservoirs, understand the geology, and build numerical models to help better produce hydrocarbon. On the other hand, conventional sedimentologists try to simulate the natural process of sedimentation in laboratory through miniature sand box models to better understand such processes. But a proper integration of the laboratory-based techniques in developing subsurface reservoirs models was always lacking in the industry. Petroleum geologists developed computer based geostatistical techniques based quantitative statistics like variograms, histograms to develop stochastic models of reservoirs which could be used to put a number and range on the geological uncertainty. However, geostatistics deals more with regularly sampled data, describing their spatial variability and directionality. In development oil fields with many wells sampling the reservoir, geostatistics helps us to create a more predictive subsurface reservoir model. However, in the exploratory state of a field with few drilled wells, the data for geostatistical analysis reduces and a robust conceptual geological is needed to build a predictive subsurface geological model where a proper integration of sedimentology and petroleum geology is required. Different approaches like conceptual block diagrams of depositional models, average sand distribution maps, training images from present day analogs were tried. However, these were less than optimal, deterministic with a long turnaround time for any robust subsurface reservoir model. A relatively recent addition to the geologist's set of quantitative tools has been Geologic Process Modeling (GPM), also known as Forward Stratigraphic Modeling (FSM) technique. This technique aims to digitally model the natural processes of erosion, transport and deposition of clastic sediments, as well as carbonate growth and redistribution based on quantitative deterministic physical principles (Cross 1990; Tetzlaff & Priddy 2001; Merriam & Davis 2001). The results show the geometry and composition of the stratigraphic sequence as a consequence of sea-level change, paleogeography, paleoclimate, tectonics and variation in sediment input. In other words, GPM brings the sedimentologists laboratory sandbox model to a petroleum geologist in his computer with the opportunity of unlimited experimentation. GPM technique is based solely on numeric modeling of open-channel flow, currents, waves, and the movement of sediment. The observed stratigraphy is the result of modeling a physical system which can then be further used for refinement in a geological facies model. (Tetzlaff et. al 2014) In the current study a 3D reservoir model for a field in Western Offshore India was built based on the results of Geological Process Model (GPM) for the thin deltaic reservoir sands as understanding reservoir continuity from seismic data was not possible. With only 4 wells available in the field, traditional geostatistics based reservoir models were inadequate in explaining the reservoir distribution. GPM based techniques helped not only in mapping the reservoir continuity but also opened up new areas for exploration in the area.
Prediction of fracture porosity and permeability remains a challenge for fractured carbonate reservoirs. As natural fractures are heterogeneous and subseismic in scale, core data and image logs only provide partially sampled data, leading to sparse information on fracture length, height, orientation, spacing, and aperture. In the present study, an integrated discrete fracture network was generated that is capable of predicting fracture porosity in Eocene carbonates of the Bengal Basin in northeastern India. The predictive fracture modeling method used 3D kinematic and geomechanical restoration of interpreted seismic horizons to estimate infinitesimal stress and strain values and to characterize associated fracture sets. Seismic attribute analysis was used to extract faults and fractures from an ant-track cube, which provided sharper definition of discontinuities seen in conventional curvature attribute data. An integrated discrete fracture model was created using information from seismic attributes, seismic inversion, and strain distribution to determine fracture intensity. Faults and fractures that are seismically resolved were statistically analyzed, which indicated that spatial distribution of fracture length follows a power law. Based on theoretical concepts of fracture mechanics, linear aperture-to-length scaling was used to characterize aperture population. A present-day geomechanical earth model was used to identify open fracture sets. This model shows that northeast–southwest-oriented fracture sets are critically stressed and will contribute to porosity and permeability. Criticality of fractures to shear failure was analyzed by computing geomechanical parameters — slip stability and dilation tendency, based on the direction and magnitude of far-field stresses. Fractures with slip and dilation tendencies greater than 0.6 in the modeled discrete fracture network were taken as inputs for porosity and permeability estimation.
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