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Petroleum Engineering – The Society of Petroleum Engineers (SPE) scholarship funds are available from many sources for students interested in pursuing careers in the oil and gas industry. Scholarships are offered throughout the world by SPE, the SPE Foundation, local SPE sections, and others. Geology – The Association of Environmental & Engineering Geologists (AEG)Foundation is dedicated to advancing research, education, and public awareness in the field of engineering geology and the related fields of environmental geology and hydrogeology. The programs of the Foundation are supported by public and corporate donations and by bequests. The AEG Foundation manages ten funds, including four scholarships.
Mitra, Abhijit (MetaRock laboratories) | Kessler, James (Occidental petroleum Corporation) | Govindarajan, Sudarshan (MetaRock laboratories) | Gokaraju, Deepak (MetaRock laboratories) | Thombare, Akshay (MetaRock laboratories) | Guedez, Andreina (MetaRock laboratories) | Aldin, Munir (MetaRock laboratories)
The magnitude of elastic anisotropy in shale is a function of composition, texture, and fabric. Rock components such as mineralogy, organic content, clay mineral orientation, alignment of matrix pore and intraparticle kerogen pores, as well as the distribution of cracks, fractures, and other discontinuities can influence anisotropy. Elastic anisotropy has a significant impact on seismic waveform interpretation, time-depth models, and stress characterization used in drilling and well completion design. Anisotropy can be estimated explicitly from core measurements but the time and budgetary requirements to conduct extensive laboratory measurements are usually prohibitive in an operating environment. Existing models aimed at characterizing anisotropy from log data involve some assumptions that may not be realistic in every formation or lithology type. We aim to predict anisotropy from log data as a function of lithotype defined primarily by mineralogy, organic content, and porosity.
This paper presents a workflow to identify lithotypes based on mineralogy, organic content, and porosity in core data from a single well and then predict elastic anisotropy for each lithotype away from the cored interval and in other wells. The workflow employs a multi-disciplinary experimental program using geology, engineering, and data analytics techniques to interpret data from core samples and log data obtained from a well in the Permian basin. First, we derive the relationship between stiffness anisotropy and lithotypes defined in core. Second, we derive the relationship between lithotype and electrofacies from log data using machine learning techniques like principal component and clustering algorithms. We then apply the predictive models to estimate anisotropy for each lithotype and test the predictive capability in the source well.
Analyses of laboratory measurements reveal that anisotropy is not significantly influenced by any single mineralogical constituent, volume of organic material, or porosity. However, a multiple linear regression model that utilizes all three of those constituents measured from core is successful in predicting anisotropy for lithotypes identified from machine learning techniques. There is good agreement between measured and modeled anisotropy when applied as an upscaling tool using well log data. Further work will test the predictability of the model in a blind test well by comparing modeled results with core and log data that are independent of this analysis.
This paper successfully applies a combination of traditional geological and engineering applications with new machine learning techniques to characterize lithotypes and predict rock properties such as elastic anisotropy. The technique avoids the assumptions used in existing models to characterize anisotropy and provides the foundational workflow that can be utilized to predict other rock properties for a variety of applications in the unconventional oilfield.
Elastic wave velocities are often used to interpret formation properties, such as porosity, mineralogy, and lithology. Velocity systematics are valuable in creating elastic models when only P-wave sonics exists in legacy wells. Although considerable research has been carried out on conventional reservoir velocity systematics, the equivalency for unconventional formations remains ill defined. In this study, a Vp–Vs systematic is developed for the Meramec formation, using laboratory pulse transmission ultrasonic measurements. The influences of porosity and mineralogy on velocities are discussed and a comparison between Meramec velocity systematic and literature systematics is provided.
The Vp and Vs measurements on 385 dodecane saturated core samples (106 vertical and 279 horizontal plugs), from seven Meramec wells were acquired. Porosity and mineralogy were also measured on each core plug. We propose two approaches to estimate Vs from Vp: 1) ignoring anisotropy, we combine both Vp and Vs measurements from all vertical plugs and low anisotropy horizontal plugs to create a single systematic, and 2) considering anisotropy, Vp measurements from horizontal plugs were corrected based on the Thomsen's compressional wave anisotropy parameter and the systematics were generated.
Meramec formation has weak shear wave anisotropy, typically <10%. Analysis shows that velocities are more sensitive to porosity than mineralogy by a factor of approximately 10. The Vp and Vs dependencies are shown below (φ is the volume fraction pores, Clays is the weight fraction clay, using vertical and horizontal samples has low anisotropy):
The shear wave systematics for dodecane saturated measurements are (All velocities are km/s.):
The first equation has a slightly smaller residual and estimated error than the second equation. Using the first equation, the Meramec velocity systematic shows good agreement with dipole wireline measurements even though there is a substantial difference in measurement frequencies. The Meramec velocity systematics are considerably different from published systematics.
The Meramec shear-wave systematics have been generated which can be applied in wireline and seismic analyses. The result shows that the method of ignoring anisotropy provides a better Vs estimation than the method considering anisotropy. However, the second method can be potentially applied to a formation that has high anisotropy. Using literature shear-wave velocity systematics may cause an estimation error of more than 16%. It is critical to generate specific velocity systematics which are calibrated to the formation of interest.
This short course will discuss various "Industrial Internet of Things (IIoT)" technologies and their application to oilfield automation. The purpose of IIoT's is to integrate sensing, communications and distributed analytics capabilities in helping the petroleum industry better manage existing assets at lower TCO. The goal is improving asset visibility and reliability, optimizing operations, and creating new value. The emergence of IOT's in oilfield operation require new skill sets and this course is intended to update the oilfield professionals. This course is for oilfield professionals who would like to expand their knowledge and skill-set in Automation of oilfields using IIoT.
Data science and machine learning are growing fields that have applications in any type of industry and has shown to improve the profit of companies that implement a data science group in them. Recently, companies from the Oil&Gas industry are starting to get on board of this new tendency and are creating and implementing new technologies with the help of machine learning algorithms. Demand for data scientists is increasing every year as new methods are required on each industry.
This course is a survey of microseismic imaging of hydraulic fracturing. It is designed to give the attendees a rudimentary understanding of this technology based on the science at its foundation, the means and methods by which it is carried out, and the benefits it brings to the users. Since this technology is interdisciplinary, combining geophysics, geology, and geomechanics with well completion technologies, the goal of the course is to give attendees the knowledge and realistic expectations of microseismic imaging of hydraulic fracturing. To this end attendees should expect to become knowledgeable and discerning users, evaluators, and questioners of those vending this technology. From its beginning, microseismic imaging of hydraulic fracturing has created controversy.
All professionals--scientist, engineers, etc.--must write. This includes those annoying reports, proposals, and all their cousins, most of which do not receive proper external editing and many of which barely satisfy their audiences. In addition, many professionals must publish. Here the audiences are more diffuse-- the next office, next floor, next building, cross town, cross the country, or cross the world--and here, again, many of these documents barely satisfy or fall short of satisfying their audiences. Simply, work not published is work not recognized and credit not received.
This unique experience will be held 12-14 October 2020 at the George R. Brown Convention Center in Houston, Texas, USA. These multi-day, hybrid events showcase top industry executives and game-changing leaders who will present their strategies and perspectives through panel, special, and technical sessions. For the first time, attendees will have open access to both societies' technical programs, a combined exhibit floor and joint networking events taking place in Houston. By co-locating these meetings, attendees will be able to leverage greater knowledge sharing and opportunities to expand business relationships. Due to the Colorado Convention Center becoming unavailable for use in October, SPE had to find a new venue for its Annual Technical Conference and Exhibition (ATCE).
This short course will discuss various “Industrial Internet of Things (IIoT)” technologies and their application to oilfield automation. The purpose of IIoT’s is to integrate sensing, communications and distributed analytics capabilities in helping the petroleum industry better manage existing assets at lower TCO. The goal is improving asset visibility and reliability, optimizing operations, and creating new value. The emergence of IOT’s in oilfield operation require new skill sets and this course is intended to update the oilfield professionals. This course is for oilfield professionals who would like to expand their knowledge and skill-set in Automation of oilfields using IIoT.