Big Data Integration Framework for Processing Petrophysical Data

Abstract

Since the introduction of the first electrical resistivity well log by Marcel and Conrad Schlumberger in 1927, the field of petrophysical well logging experienced significant technological advancements [3]. One of the new technologies was Logging While Drilling (LWD), which allows for real time data streaming and acquisition from the initial drilling depth to the target depth. The target depth sometimes reaches more than 25,000 feet, resulting in wealth of captured data [7]. As special logging probes scan given subsurface intervals, a long list of diverse readings is collected as functions of either depth or time [4]. Unfortunately, most of the obtained data cannot be used as is; several processing, calibration and interpretation activities must be performed on the stored raw data to extract useful insights about the penetrated formations [5]. While these data processing activities are plausible for one particular hydrocarbon reservoir using conventional processing techniques, performing field-wide petrophysical studies can be a real challenge. However, big data technologies can be seen as a potential solution as petrophysical data satisfies the main characteristics of big data. Such characteristics include the high volume, velocity, extreme variety of measurement types and formats, and the uncertain veracity of data attained from several vendors and sensors. In this paper, we first review the major challenges limiting geoscientists, geophysicists and petroleum engineers from fully exploiting petrophysical data. Then, we propose a big data-based framework which can help overcome some of these challenges by capitalizing on advanced processing techniques. Finally, we discuss the results of applying the framework on a defined business case.