Shale brittleness is one of the most important parameters to assess how the shale behaves upon subjecting to applied stress and evaluate the hydraulic fracturing treatment. Presence of lamination is a common feature in organic-rich shales which significantly create anisotropy in elastic properties and rock brittleness due to the platy minerals such as clays that have the tendency to be aligned in parallel orientation during burial and the digenesis process. Characterization of anisotropy and the understanding the controlling factors on the reservoir rock elastic properties, rock strength and rock brittleness are crucial for successful production and development of shales. The objective of this paper is to extend the previous discussion by (Ibrahim et al 2019), in which an integrated approach has been developed for evaluating the shale fracability, to explain the influences of shale lamination and emphasize on the effects of anisotropic in elastic properties on brittleness of organic-rich shales to better demonstrate the process of screening hydraulic fracturing candidate intervals and improve the hydraulic fracturing design which can eventually improve the production forecast. In this paper, we propose the vertical transverse isotropic (VTI) modeling to investigate the effect of shale lamination and anisotropy on rock elastic properties, tensile failure and wave velocity normal to bedding plane, which differ than they are when parallel to bedding plane. Throughout this study, it is observed that there is a remarkable effect of anisotropy parameters on rock elastic properties and tensile failure. This method help obtain more accurte brittleness index and give precise guide to optimize perforation depths choice and hydraulic fracturing design that can result in optimized hydrocarbon productivity.
Within a single field geophysical survey results always have a significant amount of data with a considerable variability and heterogeneity. This allows to classify geophysical data as a Big Data. Data scientists and software developers are increasingly recommending the use of machine learning techniques for data processing and interpretation. ML algorithms allow one to extract the most complete amount of useful information, reduce time costs, minimize the subjective factor in the decision-making process, etc. Early testing of these approaches began in the 60s, active practical implementation consisted in the 90s due to the large-scale implementation of seismic studies in 3D CDP modification 1. The emergence of new algorithms, modifications of the original data, the development of computational resources support the relevance of this topic at the present time. In seismic data interpretation machine learning approaches provide high performance in the process of automatic horizons picking, fault tracing, seismic facies analysis, sesimic inversion, reservoir prediction, etc. At the stage of seismic facies analysis application of the ML algorythms is especially effective since in the process of multiattribute classification the initial dataset increases severalfold in accordance with the number of calculated attributes 5-7, 9, 10.
A well was drilled into a prospective new unconventional mudstone play offshore Norway. Two of five coring runs were successful while the rest yielded little to no core recovery. Investigations attributed the poor recovery to sub-optimal coring practices, equipment failure and operational errors. Recently, the accompanying petrophysical logs and seismic data were revisited, and upon detailed investigation several unusual responses were observed to correspond with intervals of poor core recovery. Subsequent investigation of the core itself substantiated that the coring issues largely had natural causes. This understanding is being applied to two imminent coring operations and has driven selection of drilling, coring and wireline technology and procedures, in addition to informing casing design.
Wireline nuclear magnetic resonance (NMR) and cross dipole acoustic data, logging whilst drilling (LWD) density (including azimuthal images), neutron porosity and resistivity was acquired over the interval of interest for standard formation evaluation purposes. This interpretation was conducted immediately after the initial drilling and showed the formation to be a series of highly porous oil bearing mudstones. However, no in depth advanced interpretation was conducted at the time. Recently, advanced analysis including high resolution log enhancement, NMR 2D porosity and saturation analysis, acoustic azimuthal anisotropy, near wellbore imaging, fracture interpretation, and borehole image interpretation were performed on the log data, and new and improved 3D seismic data was interpreted. When interpreted in detail it could be observed that unusual responses in the logs showed a close correspondence to the intervals of poor core recovery. In particular, high azimuthal anisotropy was observed, and when this was compared to the near wellbore reflection image a significant planar reflecting feature was identified which is determined to be a fault. Indications of this feature was subsequently found in seismic data. When then compared to the azimuthal density image after resolution enhancement was applied, although the image is still of too low resolution to directly image the fault, disturbed bedding was observed which is commonly associated with faulted intervals. Several core fragments proved to have extensive small-scale fracturing not noticed previously, and slickenlines were found along several larger fractures previously presumed to be drilling induced.
The investigations of the log data revealed that a previously unknown sub-seismic fault was present right below the depth where coring problems were encountered. The detailed interpretation was able to determine the precise location of the fault and its extent in the formation. Knowledge of this subsequently explained the coring problems encountered and helps to optimise imminent coring in the same formation. Lessons learned and the methodology likely also applies to similar formations.
In this paper we discuss coring issues encountered in a new and unconventional play offshore, present new data and interpretation that sheds light on them and describe the methodology of the detailed integrated interpretation that uncovered the previously unknown root cause. We then discuss how these findings can be (and are) used to optimise both drilling, coring, and logging operations in future wells.
Gong, Yiwen (The Ohio State University) | Mehana, Mohamed (Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, USA) | Xiong, Fengyang (The Ohio State University) | Xu, Feng (Research Institute of Petroleum Exploration and Development CO., LTD, CNPC) | El-Monier, Ilham (China National Oil and Gas Exploration and Development Corporation)
Rock elastic moduli are one of the major perspectives for the hydraulic fracturing design. Among all of them, Young's modulus and Poisson's ratio essentially control fracture aperture for the proppant placement. The objective of this work is to predict the elastic moduli by applying data mining techniques as a comparison to the experimental measurements. We have collected attributes representing the pore structure, mineralogy and geomechanical characteristics. We implemented classification techniques such as k-means, hierarchical and PAM (partition around medoids). PAM results in more evenly-distributed clusters compared to the rest. Artificial Neural Network (ANN) is used for regression. We formulated two scenarios; firstly, all the data is grouped into one group and the other involves performing the regression on the clustered data. Interestingly, both scenarios yield acceptable results. The classification results could guide the fracturing operations where clusters with high brittleness, low anisotropy and high microfracture intensity could be identified as fracture candidates. Still the main limitation to unleash the machine learning capabilities in this domain is the data scarcity
This paper has an objective of identifying the nature of formation fluid from an extreme tight fractured reservoir. A good understanding of petrophysical properties of the reservoir rock as well as the fluid it contains constitutes a real challenge for tight reservoirs, that are the most common unconventional sources of hydrocarbons. The front-line characterization mean is the Wireline logging which comes directly after drilling the well or while drilling, knowing that for low to extreme low porosity-permeability reservoirs any attempt of conventional well testing will not bring any added value not rather than a confirmation of reservoir tightness. A tailored workflow was adopted to design the most appropriate formation testing module, select the best depths for fluid sampling, and distinguish hydrocarbon from water bearing intervals. This workflow involves ultrasonic and Electric Borehole Images in combination with Sonic Scanner for natural fractures detection, localization and characterization, integrating Dielectric recording and processing for petrophysical evaluation, then Formation Testing was carried out for fluid identification and sampling. The use of borehole electric and sonic imager coupled with advanced sonic acquisition helped not only to identify the natural fractures depths, but also the nature of these fractures. This integration was used for selecting the sampling station.
Glover, Paul W. J. (University of Leeds) | Lorinczi, Piroska (University of Leeds) | Al-Zainaldin, Saud (University of Leeds) | Al-Ramadhan, Hassan (University of Leeds) | Sinan, Saddam (University of Leeds) | Daniel, George (University of Leeds)
New reservoirs are increasingly more heterogeneous and more anisotropic. Unfortunately, conventional reservoir modelling has a resolution of only about 50 m, which means it cannot be used to model heterogeneous and anisotropic reservoirs effectively when such reservoirs exhibit significant inter-well variability at scales less than 50 m. This paper describes a new fractal approach to the modelling and simulation of heterogeneous and anisotropic reservoirs. This approach includes data at all scales such that it can represent the heterogeneity of the reservoir correctly at each scale.
Three-dimensional Advanced Fractal Reservoir Models (AFRMs) can be generated easily with the appropriate code. This paper will show: (i) how 3D AFRMs can be generated and normalised to represent key petrophysical parameters, (ii) how these models can be used to calculate permeability, synthetic poro-perm cross-plots, water saturation maps and relative permeability curves, (iii) the effect of altering controlled heterogeneity and anisotropy of generic models on fluid production parameters, and (iv) how AFRMs which have been conditioned to represent real reservoirs provide a much better simulated production parameters than the current best technology.
Results of generic modelling and simulation with AFRMs show how total hydrocarbon production, hydrocarbon production rate, water cut and the time to water breakthrough all depend strongly both on heterogeneity and anisotropy. The results also show that in heterogeneous reservoirs, the best production data is obtained from placing both injectors and producers in the most permeable areas of the reservoir – a result which is at variance with common practice. Modelling with different degrees and directions of anisotropy shows how critical hydrocarbon production data depends on the direction of the anisotropy, and how that changes over the lifetime of the reservoir.
We have developed a method of fractal interpolation to condition AFRMs to real reservoirs across a wide scale range. Comparison of the hydrocarbon production characteristics of such an approach to a conventional krigging shows a remarkable improvement in the modelling of hydrocarbon production when AFRMs are used; with AFRMs in moderate and high heterogeneity reservoirs returning values always within 5% of the reference case, while the conventional approach often resulted in systematic underestimations of production rate by over 70%.
These methods use the crossed-dipole shear to derive azimuthal anisotropy and the Stoneley wave to derive TI anisotropy in slow formations, or a combination of these modes in deviated wells. A reasonable shear velocity can be derived using inversion techniques with low-frequency Stoneley-wave dispersion which is sensitive to the horizontal shear (in contrast to the dipole's sensitivity to the vertical shear).
Locating fractures, recognizing fracture morphology, and identifying fluid-flow properties in the fracture system are important criteria in characterizing reservoirs that produce predominantly from fracture systems. Acoustic techniques can provide insight. Fracture identification and evaluation using conventional resistivity and compressional-wave acoustic logs is difficult, in part because fracture recognition is very dependent on the dip angle of fractures with respect to the borehole. Fractures are physical discontinuities that generate acoustic reflection, refraction, and mode conversion--all of which contribute to a loss of transmitted acoustic energy. In particular, compressional- and shear-wave amplitude and attenuation and Stoneley-wave attenuation are significantly affected by the presence of fractures.
Low-frequency ( 1 kHz) dipole sources allow for shear-velocity determination that is much closer to seismic shear waves and permits acquisition of direct-shear velocities in slow and fast formations. However, increased noise (i.e., a lower signal-to-noise ratio) is one limitation of low-frequency operation. Noise has been reduced through improved acquisition electronics, the use of semi-rigid tool designs, and by choosing the operational mode of the dipole source. A semi-rigid tool body not only reduces the influence of the tool body on the measurement but also permits operation in deviated wells. At high frequencies, or when the borehole diameter is large, flexural-mode propagation is slower and a dispersion correction is needed to obtain the shear velocity from the measured flexural velocity. This dispersion correction is a function of mud compressional velocity, formation compressional and shear velocities, the ratio of formation and mud densities, and the product of borehole diameter and processing frequency. Few, if any corrections are required if the flexural wavelength (velocity/frequency) is at least three times the borehole diameter, which is why low frequencies ( 1 kHz) are used.
Elastic waves are comprised of compressional (or P-waves) and shear (or S-waves). In compressional waves, the particle motion is in the direction of propagation. In shear waves, the particle motion is perpendicular to the direction of propagation. Understanding the velocity of these waves provide valuable information about the rocks and fluids through which they propagate. Stress strain relationships in rocks considered only the static elastic deformation of materials.