Petrophysics is a pivotal discipline that bridges engineering and geosciences for reservoir characterization and development. New sensor technologies have enabled real-time streaming of large-volume, multi-scale, and high-dimensional petrophysical data into our databases. Petrophysical data types are extremely diverse, and include numeric curves, arrays, waveforms, images, maps, 3-D volumes, and texts. All data can be indexed with depth (continuous or discrete) or time. Petrophysical data exhibits all the "7V" characteristics of big data, i.e., volume, velocity, variety, variability, veracity, visualization, and value. This paper will give an overview of both theories and applications of machine learning methods as applicable to petrophysical big data analysis. Recent publications indicate that petrophysical data-driven analytics (PDDA) has been emerging as an active sub-discipline of petrophysics. Field examples from the petrophysics literature will be used to illustrate the advantages of machine learning in the following technical areas: (1) Geological facies classification or petrophysical rock typing; (2) Seismic rock properties or rock physics modeling; (3) Petrophysical/geochemical/geomechanical properties prediction; (3) Fast physical modeling of logging tools; (4) Well and reservoir surveillance; (6) Automated data quality control; (7) Pseudo data generation; and (8) Logging or coring operation guidance. The paper will also review the major challenges that need to be overcome before the potentially game-changing value of machine learning for petrophysics discipline can be realized. First, a robust theoretical foundation to support the application of machine leaning to petrophysical interpretation should be established; second, the utility of existing machine learning algorithms must be evaluated and tested in different petrophysical tasks with different data scenarios; third, procedures to control the quality of data used in machine leaning algorithms need to be implemented and the associated uncertainties need to be appropriately addressed. The paper will outlook the future opportunities of enabling advanced data analytics to solve challenging oilfield problems in the era of the 4th industrial revolution (IR4.0).
Rock typing in carbonate reservoirs is challenging due to high spatial heterogeneity and complex pore structure. In extreme cases, conventional rock typing methods such as Leverett's J-function, Winland's R 35 , and flow zone indicator are inadequate to capture the heterogeneity and complexity of carbonate petrofacies. Furthermore, these methods are based on core measurements, hence are not applicable to uncored reservoir zones. This paper introduces a new method for petrophysical rock classification in carbonate reservoirs that honors multiple well logs and emphasizes the signature of mud-filtrate invasion. The method classifies rocks based on both static and dynamic petrophysical properties. An inversion-based algorithm is implemented to simultaneously estimate mineralogy, porosity, and water saturation from well logs. We numerically simulate the process of mud-filtrate invasion in each rock type and quantify the corresponding effects on nuclear and resistivity measurements to derive invasion-induced well-log attributes, which are subsequently integrated into the rock classification. Under favorable conditions, the interpretation method advanced in this paper can distinguish bimodal from uni-modal behavior in saturation-dependent capillary pressure otherwise only possible with special core analysis. We successfully apply the new method to a mixed clastic-carbonate sequence in the Hugoton gas field, Kansas. Rock types derived with the new method are in good agreement with lithofacies described from core samples. The distribution of permeability and saturation estimated from well-log-derived rock types agrees with routine core measurements, with the corresponding uncertainty significantly reduced when compared to results obtained with conventional porosity-permeability correlations.
Summary Geoscientific and engineering experiments in petrophysics, rock physics, and rock mechanics depend on multiple, costly, and sometimes rare samples used to characterize the properties of natural rocks. Testing these samples helps in modeling various hydrocarbon recovery and stimulation scenarios, as well as understanding the fluid-rock interactions in the subsurface under various pressure and temperature conditions. Over the last decade, 3D printing has matured to become a more commonly available tool to enable repeatable experiments with controllable materials and pore system geometries to investigate petrophysical, geomechanical, and geophysical properties of porous rocks. This review introduces the development, characteristics, and capabilities of 3D printing technology that are specifically used in research. Applications in the realm of petrophysics highlight the issues of replicating the pore network geometry and subsurface physics, aiming at understanding fluid flow in porous media problems. Using 3D-printed models in rock mechanics experiments focuses on generating comparable geomechanical properties and reproducing fractures, joint surfaces, and other rock structures, whereas in rock physics, geophysical forward modeling is highlighted to take advantage of 3D printing technology. By summarizing the recent advances in 3D printing as applied to petrophysics, rock physics, and rock mechanics, this review paper presents the current state of the art and the challenges in scale, cost, time, and materials, as well as the directions for advancing this frontier discipline to answer various fundamental questions regarding porous media research using 3D printing technology.
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