Band 3, Heft 4

In the last decade, the unconventional resource has changed the global energy and petroleum industry. The technological innovations of hydraulic fracturing and horizontal drilling techniques, which require a good understanding of the rock mechanical properties of shale, have driven the economical production of shale reservoirs. However, the intrinsic rock-mechanical properties of shale rocks are extremely complex; they are affected by many factors, including confining pressure, water content, water salinity, TOC (total organic carbon), clay content, bedding plane orientation, mineralogy, anisotropy and others. Some factors ( i.e. , water content, TOC and clay content etc.) can significantly decrease the strength of shale rocks, and this weakening mechanism needs to be considered in hydraulic fracture designing and wellbore stability. This paper systematically investigates various key factors that impact the rock-mechanical properties especially the mechanical properties of shale rocks, including confining pressure, water content, TOC (Total organic carbon), clay content, bedding plane orientation, porosity, anisotropy, and temperature effects. We experimentally investigated the impact of water saturation on Barnett shale’s rock-mechanical properties (Young’s modulus, E and Poisson’s ratio, v and uniaxial unconfined compressive strength (UCS)) using a Material Testing System (MTS-810). Experimental results showed that: 1) water saturation has a significant impact on Young’s modulus and UCS. Young’s modulus decreases 6.1% with an increase of a water saturation (1%) of Barnett shale core corresponding to the value of dry shale’s Young’s modulus; 2) Young’s modulus and UCS decrease linearly with increasing water saturation; And 3) the relationship between water saturation and Poisson’s ratio is not obvious.

As the use of subsurface reservoirs for energy extraction, wastewater disposal, and geologic storage of CO 2 grows, so do concerns about the potential for unwanted interactions between subsurface and surface receptors. Wellbores are engineered fluid flow pathways designed to facilitate the production and injection of fluids in a controlled manner. Because of the coupling between wellbore age and integrity, regions with prior drilling histories may be particularly susceptible to unwanted fluid migration if disturbed by contemporary subsurface activities. Until recently, wellbore drilling records were prone to a range of error sources. In addition, record coverage and quality varies based on local, state, tribal, and federal jurisdictions. As a result, there is a significant amount of spatial and temporal variation in reporting standards, and consequent data content, preservation, and access to information. The recent development of subsurface reservoirs has accelerated the need for a standardized methodology to characterize and capture uncertainty in these records. This study develops and demonstrates a flexible approach for the meso-scale collection, analysis, and communication of uncertainty in wellbore datasets. It is shown that incorporating publicly available data from a variety of digital and non-digital sources can inform characterization and uncertainty of the locations of wellbores.

In an effort to better understand the well performance in one of the Chevron’s assets in San Joaquin Valley, a study was conducted to evaluate the perforation strategies and capture best practices. Well completion through perforation is typically performed using bare essential technology such as wireline logs and perforation guns. For basic reservoir formations, simple rules-of-thumb are used for perforation spacing and interval lengths. These are rarely validated by other methods, such as production logging and micro-seismic monitoring. For more challenging lithology, a more appropriate approach would be to place perforation clusters in target formations with similar properties. The research presents an efficient use of fuzzy clustering technology for identification of the optimum perforation strategy in a challenging waterflood diatomite reservoir. The methodology was applied on all newly drilled wells in the reservoir (within the last two years), and we found that this new approach improved our understanding over previous practices, not only by designing optimum perforations, but also an increased production was observed. Cluster analysis is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other (in some sense or another) than to those in other clusters. There are two commonly used types of clustering methods: hard and fuzzy clustering. In hard clustering, data is divided into distinct clusters, where each data element belongs to exactly one cluster. In fuzzy clustering, data elements can belong to more than one cluster, and associated with each element is a set of membership levels. The fuzzy clustering algorithm, also known as Fuzzy C-Mean (FCM) algorithm, was applied to log data of wells from different areas of a reservoir. Based on the clustering results, the workflow then identified whether the perforation was performed on “good” regions (sand) or on “bad” regions (shale bedding). This information allowed the evaluation of the perforation jobs executed and also allowed capturing best practices and design changes for future well completions. The case study presents a simple, yet efficient workflow to extract additional information from logs and improve completion strategies and perforation design. The methodology is flexible and can be applied to any well where complex lithology creates a challenge in defining the optimum perforation intervals

Gold (Au) nanoparticles are incorporated into the P3HT/PCBM active layer of organic solar cells in order to enhance their power conversion efficiency (PCE). In addition, a zinc oxide (ZnO) buffer layer was assembled on top of the P3HT/PCBM/Au-NPs active layer in order to increase the charge mobility across the cells. The incorporation of Au-NPs in the active layer and the addition of a ZnO buffer layer increased the power conversion efficiency by approximately 60%, compared to a reference cell without Au-NPs and a buffer layer. The short circuit current ( J sc ) of the cells containing 0.5 mg of Au-NPs and a ZnO buffer layer was measured at 7.381 mA/cm 2 compared to 5.225 mA/cm 2 in the reference cells; meanwhile, the external quantum efficiency (EQE) increased from 61.25% to 68.23%, showing an enhancement of 11.4%. Furthermore, Au-NPs improved the charge collection at the anode, which resulted in a higher short circuit current and fill factor. The strong near field around Au-NPs due to a localized surface plasmonic resonance (LSPR) effect enhanced the light absorption in the active layer, and hence the overall electrical performance of the cells.