Impact of lithofacies variations and structural changes on natural fracture distributions

Abstract

Understanding and predicting the impact of lithofacies changes and structural effects on fracture distributions is vitally important to optimize a drilling location and orientation. To evaluate and model fracture intensity of the Late Ordovician-Silurian-Early Devonian Hunton Group carbonates in Oklahoma, natural fractures were studied at different scales using borehole images, three outcrops (two horizontally bedded outcrops and one anticline outcrop), and seismic data. Natural fractures identified from eight horizontal well borehole images include conductive (open), partially open, mineralized (closed), and induced fractures. Four fracture sets were identified from borehole images and from the two horizontally bedded outcrops. A 3D fracture intensity model was populated, from the fracture intensity logs at the boreholes, and compared with a 3D lithofacies model. Principal component analysis from lithology logs produced input to a self-organizing map to classify and cluster electrofacies. Thin sections and borehole images corroborate the electrofacies around the wellbores, whereas 3D seismic data were used as constraints to build a 3D lithofacies model. A 3D lithofacies model resulted from the extrapolation of the lithofacies from the well scale to the regional seismic scale. In this study area, lithofacies and structure are interrelated and control fracture distributions. Lithofacies is the primary control, whereas structure is the secondary control. Three lithofacies (wackestone, mudstone, and mud-dominated wackestone) were identified. A positive relationship between the fracture intensity and the presence of wackestone was observed at well locations and in the mapped subsurface area. The other two lithofacies do not exhibit high fracture abundance. Structural effects influence fracture distributions near faults and positive curvature areas in the subsurface measured on the 3D seismic data. For the Hunton Anticline outcrop exposure, there was a positive linear relationship between fracture intensity and changes in curvature for the mudstone and mud-dominated wackestone and an exponential relationship for the wackestone textures. The integration of lithology and structure from multidisciplinary, multiscalar data (i.e., outcrops, image logs, and 3D seismic) helps to identify and predict the fractured zones in the Hunton carbonates and can be used for horizontal well planning as well as stimulation programs. More importantly, this study proposes a generic model to predict the variability of fractures at different scales of curvatures combined with lithology changes and can be used for other carbonate reservoirs.

Practical Aspects of Upscaling Geocellular Geological Models for Reservoir Fluid Flow Simulations: A Case Study in Integrating Geology, Geophysics, and Petroleum Engineering Multiscale Data from the Hunton Group

“…The Hunton Group, including the Chimneyhill subgroup, is associated with a shallow-warm water carbonate ramp of a gently sloping surface with less than one degree dip [18,19]. In the area of study (Figure 2), the stratigraphic thickness ranges from 50 to 120 ft. Three primary lithologies of the Chimneyhill in the study area include wackestone, mudstone, and mud-dominated wackestone [20]. The Hunton Group facies comprise a series of progradational and aggradational parasequences (shallowing-upward cycles) [21].…”

“…We measured porosities as well as vertical and horizontal permeabilities from core plugs facilitating the construction of porosity-permeability relationships. Following previous work by Milad et al [11], Milad and Slatt [20], Milad et al [27], we constructed vertical variograms from the well logs to populate the petrophysical data spatially.…”

Application of Geologically Constrained Machine Learning Method in Characterizing Paleokarst Reservoirs of Tarim Basin, China

“…Although in-depth understanding and training of geophysics is considered a prerequisite for developing effective interpretive workflows, recent advances in machine learning have provided new insights into its role in this particular domain problem, such as self-organizing diagrams [6,7], multilayer perceptron [8,9], and K-means clustering [10,11]. Machine learning models can interpret large amounts of data and understand the relationships of various types of data at the same time, providing repeatable and reliable results for seismic interpretation [12].…”

X-Ray Fluorescence Applications in Mudrock Characterization: Investigations into Middle Devonian Stratigraphy, Appalachian Basin, USA

“…Mudrock reservoirs are characterized by nanometer-to micrometer-size pores and nano-to microdarcy permeability (Loucks et al, 2012;Peng and Loucks, 2016;Milad and Slatt, 2018).…”

“…In addition to rock brittleness, natural fractures play a significant role in the effectiveness of hydraulic stimulation (Olson et al, 2001;Rijken, 2005;Gale and Holder, 2007). It has been hypothesized that natural fractures form as a result of potentially one or a number of mechanisms, including tectonic events that cause local and regional stress changes, uplift, differential compaction, strain from the accommodation of large structures, and catagenesis (Neuzil and Pollock, 1983;Jowett, 1987;Price, 1997;Rodrigues et al, 2009;Engleder et al, 2009;Gale et al, 2007;Milad and Slatt, 2018). Natural fractures, either mineralized (sealed) or open, can reactivate during hydraulic stimulation and create more complex fracture networks than could be achieved with a single hydraulic fracture (Gale and Holder, 2007).…”