ARMA 13-527 Iterative 3 D geomechanical modeling to determine distribution and impact of stresses in deepwater Gulf of Mexico subsalt and near-salt environments

A 3D finite-element model (FEM), covering an area of 300 km in the deepwater Gulf of Mexico, was used to determine the stress distribution around salt structures, and to study the impact on well design decisions. A 3D model was necessary to accurately determine the full stress tensor (i.e., changes of stress magnitude and orientation around the salt body). An iterative workflow to fine-tune properties and stresses has been developed to validate wellbore failure and to explain drilling events. The 3D FEM was built using a geological model, high-quality seismic velocity data, and results from 1D mechanical earth models. The model indicates that the stress magnitude and orientation are modified by salt morphology. In particular locations at the salt/sediment interface, this phenomenon resulted in significant stress rotation and high von Mises stress. In these circumstances, the safe drilling mud weight window can quickly disappear, forcing the operator to manage both wellbore instability and mud loss, and with no choice but to sidetrack or select a new well location. The described case study illustrates the application of the 3D FEM, which was able to explain drilling events (including an abandoned wellbore), provide input for calculating the mud weight window (range between shear failure and minimum stress), and guide well design. The significance of this study is that, as more well trajectories are drilled in these complex environments, robust, predictive 3D FEMs will be necessary to assess potential risk prior to drilling and to improve well planning. environments [2, 5‒8]. Detailed description and comparison of the modeling techniques is included in Luo et al. [4]. Numerical simulation to solve complex geomechanical problems has proven suitable; however, it has not become a routine practice due to the perceived lack of well information or lack of quality subsalt seismic velocity data prior to drilling, which makes the results uncertain. Furthermore, basin-wide models usually have low spatial resolution; therefore these models are mostly impractical for wellbore-centric studies. Finally, it is difficult to validate the results of a 3D model with wellbore data; therefore, the 3D results are often considered qualitative only. In this paper, we describe a modeling approach in which 3D numerical simulation covering 300 km (115.8 mi) was used to predict stresses around a salt body in a deepwater field in the Gulf of Mexico (Figure 1). While recent publications in which authors describe attempts to overcome the shortcomings associated with spatial resolution or data quality [8, 9] by incorporating local gridding techniques or well data and high-quality processed seismic interval velocity, respectively; we show examples that validate the 3D model by using wellbore data to explain drilling events. The calibrated iterative model was used to estimate the mud weight window for a planned well in the same prospect. The combined 3D models generated from well log-derived and locally calibrated elastic properties, strength, pressures, and stresses provide a more realistic earth model in contrast to the past published stress models, which are mostly uncalibrated and are built on spatially constant properties (layer-cake). Figure 1. Location map of the study area (magenta rectangle) in the deepwater Gulf of Mexico showing available offset wells, A, B, and C, and the reservoir top surface in the background. Scale: 1 U.S. offshore block is 4.8 km  4.8 km (3  3mi). 2. METHODOLOGY: ITERATIVE 3D MODELING 2.1. Wellbore-Centric Modeling 1D mechanical earth models (MEMs) were constructed for three existing offset wells in the area of interest (Figure 1). The MEM is defined as a mathematical representation of rock properties, pore pressure, and in situ stresses as a function of the depth for a particular interval. The development of the earth model followed a detailed workflow, which comprises ten steps [10]. Input data used to construct the models includes formation evaluation logs (compressional and shear velocity, gamma ray, density, neutron porosity, and checkshots) from wireline or logging-while-drilling operations; formation markers and lithology descriptions, drilling data with list of drilling events from offset wells, fluid and mud logging information, and petrophysical interpretations (porosity and volume of clay). Proprietary correlations for mechanical properties based on laboratory measurements on cores were adapted from a nearby field. Pore pressures and stresses were derived from available wellbore density and sonic data, and calibrated against in situ formation measurements and well events (kicks, results of leakoff test (LOT), and mud losses). 2.2. Field-Centric Modeling Factors considered while building the 3D geological model for the study area included the structural complexity of the salt (overhangs), orientations (pinchout) of the reservoir sands relative to the base of salt, and the positions of existing offset wells. In addition to the 3D interpreted surface representing the water bottom, the top and base of salt and the reservoir sand surfaces were used to generate the structural framework. In the framework model, truncation rules (e.g., erosional, base, discontinuous, and conformable) were accurately represented, as interpreted from the available seismic image data. A 3D MEM was generated on the basis of well log-derived rock properties (1D MEMs) and the available 3D seismic data. In sections where well logs were not available due to the absence of logging operations, such as in the overburden, correlations were used to predict the required properties from the seismic velocity. The 3D MEM, which incorporated reservoir geometry and mechanical properties was exported from the geological model and imported into the finite element stress modeling routine of proprietary geomechanics modeling software [11]. The geological model was extended by adding sideburdens on the four vertical sides and the underburden (below the reservoir). This improved the model’s aspect ratio and ensured a reasonable transfer of stresses within the reservoir and from the overburden in the preproduction stress phase of the modeling. The embedded model consisted of 148  72  358 I,J,K cells, for a total of 3.81 million cells. Properties in the sideburden section were assumed constant for each layer and reflected average layer values. Properties in the underburden section were extended downward from the model bottom. The initial stress state of the field was determined with finite element geomechanics software [11]. Inelastic constitutive laws for sediments surrounding the salt were applied in multiple modeling runs, while the fluid pressures were derived from 3D seismic interval velocity data, using a velocity-to-pore-pressure transform. The Mohr-Coulomb plastic yield criterion was used to model intact rock behavior. Effective stresses were used during simulations, and then were added to pore pressure to obtain the total stresses according to the effective stress principle. The salt formation was modeled as a linear elastic perfectly-plastic material (with high Poisson’s ratio, low Young’s modulus, very low cohesion, and zero pore pressure) to ensure isotropic principal stresses within the salt formation.