Three-dimensional (3D) printing is capable of transforming intricate digital models into tangible objects, allowing geoscientists to replicate the geometry of 3D pore networks of sedimentary rocks. We provide a refined method for building scalable pore-network models ("proxies") using stereolithography 3D printing that can be used in repeated flow experiments (e.g., core flooding, permeametry, porosimetry). Typically, this workflow involves two steps, model design and 3D printing. In this study, we explore how the addition of post-processing and validation can reduce uncertainty in the 3D-printed proxy accuracy (difference of proxy geometry from the digital model). Post-processing is a multi-step cleaning of porous proxies involving pressurized ethanol flushing and oven drying. Proxies are validated by: (1) helium porosimetry and (2) digital measurements of porosity from thin-section images of 3D-printed proxies. 3D printer resolution was determined by measuring the smallest open channel in 3D-printed "gap test" wafers. This resolution (400 µm) was insufficient to build porosity of Fontainebleau sandstone (∼13%) from computed tomography data at the sample's natural scale, so proxies were printed at 15-, 23-, and 30-fold magnifications to validate the workflow. Helium porosities of the 3D-printed proxies differed from digital calculations by up to 7% points. Results improved after pressurized flushing with ethanol (e.g., porosity difference reduced to ∼1% point), though uncertainties remain regarding the nature of sub-micron "artifact" pores imparted by the 3D printing process. This study shows the benefits of including post-processing and validation in any workflow to produce porous rock proxies.
Summary Diagenetic effects in carbonate rocks can enhance or occlude depositional pore space. Reliable identification of porosity-enhancing diagenetic features (e.g., vugs and fractures) is essential for petrophysical characterization of reservoir properties (e.g., porosity and permeability), construction of geological and reservoir models, reserve estimation, and production forecasting. Challenges remain in characterizing these diagenetic features from well logs as they are often mixed with variations in mineral and fluid concentrations. Herein, we explore a data-driven approach that is based on a comprehensive well log data set from the Arbuckle Formation in Kansas to classify vuggy facies in carbonate rocks. The available well log data include conventional logs (gamma ray (GRTC), resistivity (RT), neutron/density porosity (NPHI/RHOB), photoelectric factor (PE), and acoustic slowness) and nuclear magnetic resonance (NMR) transverse relaxation time (T2) logs. We parameterized the measured T2 distribution using a multimodal lognormal Gaussian density function and combined the resulting Gaussian parameters with conventional logs as inputs into three supervised machine learning (ML) algorithms; namely, support vector machine (SVM), random forest (RF), and artificial neural network (ANN). The facies labeling data used in this study were based on visual examination of vug sizes from core samples, which include five classes; namely, nonvuggy, pinpoint-size, centimeter-size, fist-size, and super-vuggy. In total, 80% of the data set was used as the training set, and a fivefold cross validation was used for hyperparameter tuning. We conducted a detailed comparison of the above three ML algorithms on the basis of different combinations of features. The highest classification accuracy achieved on the holdout testing set is 84% using SVM on a combination of conventional logs and selected NMR Gaussian parameters as inputs. In general, inclusion of conventional log data improves the prediction accuracy compared with using NMR data alone. Feature selection improves the performance for SVM and ANN but is not recommended for RF.
A Bayesian Belief Network (BN) has been developed to predict fractures in the subsurface during the early stages of oil and gas exploration. The probability of fractures provides a first-order proxy for spatial variations in fracture intensity at a regional scale. Nodes in the BN, representing geologic variables, were linked in a directed acyclic graph to capture key parameters influencing fracture generation over geologic time. The states of the nodes were defined by expert judgment and conditioned by available datasets. Using regional maps with public data from the Horn River Basin in British Columbia, Canada, predictions for spatial variations in the probability of fractures were generated for the Devonian Muskwa shale. The resulting BN analysis was linked to map-based predictions via a geographic information system. The automated process captures human reasoning and improves this through conditional probability calculations for a complex array of geologic influences. A comparison between inferred high fracture intensities and the locations of wells with high production rates suggests a close correspondence. While several factors could account for variations in production rates from the Muskwa shale, higher fracture densities are a likely influence. The process of constructing and cross-validating the BN supports a consistent approach to predict fracture intensities early in exploration and to prioritize data needed to improve the prediction. As such, BNs provide a mechanism to support alignment within exploration groups. As exploration proceeds, the BN can be used to rapidly update predictions. While the BN does not currently represent time-dependent processes and cannot be applied without adjustment to other regions, it offers a fast and flexible approach for fracture prediction in situations characterized by sparse data.
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