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Abstract Decades of subsurface exploration and characterisation have led to the collation and storage of large volumes of well related data. The amount of data gathered daily continues to grow rapidly as technology and recording methods improve. With the increasing adoption of machine learning techniques in the subsurface domain, it is essential that the quality of the input data is carefully considered when working with these tools. If the input data is of poor quality, the impact on precision and accuracy of the prediction can be significant. Consequently, this can impact key decisions about the future of a well or a field. This study focuses on well log data, which can be highly multi-dimensional, diverse and stored in a variety of file formats. Well log data exhibits key characteristics of Big Data: Volume, Variety, Velocity, Veracity and Value. Well data can include numeric values, text values, waveform data, image arrays, maps, volumes, etc. All of which can be indexed by time or depth in a regular or irregular way. A significant portion of time can be spent gathering data and quality checking it prior to carrying out petrophysical interpretations and applying machine learning models. Well log data can be affected by numerous issues causing a degradation in data quality. These include missing data - ranging from single data points to entire curves; noisy data from tool related issues; borehole washout; processing issues; incorrect environmental corrections; and mislabelled data. Having vast quantities of data does not mean it can all be passed into a machine learning algorithm with the expectation that the resultant prediction is fit for purpose. It is essential that the most important and relevant data is passed into the model through appropriate feature selection techniques. Not only does this improve the quality of the prediction, it also reduces computational time and can provide a better understanding of how the models reach their conclusion. This paper reviews data quality issues typically faced by petrophysicists when working with well log data and deploying machine learning models. First, an overview of machine learning and Big Data is covered in relation to petrophysical applications. Secondly, data quality issues commonly faced with well log data are discussed. Thirdly, methods are suggested on how to deal with data issues prior to modelling. Finally, multiple case studies are discussed covering the impacts of data quality on predictive capability.
ABSTRACT Success of directional wells is reliant on accurate identification of formation being drilled, and correction of off-shoots and out of zone trajectories by steering the well back in the correct direction. Geo-steering is the directional control of a well path based on real-time measurements assimilated while drilling and comparing the observed variables to expectations drawn from nearby wells and known formations. Directional drillers and geologists alike must make split-second geo-steering decisions that ultimately affect overall productivity of the well. Providing an accurate, predictive model to cross-check with the inflowing real-time data allows specialists to confidently keep the wellbore in the zone of interest. The present study uses geological and geophysical data from offset wells, to estimate lithological facies for the projected well trajectory. A suite of machine learning and data analytics algorithms, including Long Short-Term Memory (LSTM), are employed to extract the characteristic signatures of different lithofacies, and generate the expected signatures for the well of interest, based on the available logs from nearby wells. Upon detection of deviation from the expected path, corrective measures can be suggested to the operator, allowing for confident geo-steering. 1. INTRODUCTION The advent of downhole drill motors and directional drilling has made it possible to expose several thousand feet of reservoir to the well as potential pay, versus a few feet of vertical section that was achieved with vertical wells. This can significantly increase the contacted reservoir interval and productivity of the well. Success of directional drilling relies on accurate Geosteering, where real time inputs from Measurements While Drilling (MWD) tools are used to determine geolocation and steer the drill bit to keep the well on the desired path, and in the pay zone. The MWD tools are placed above the drill bit. MWD tools are also commonly referred to as Logging While Drilling (LWD) tools, however, it should be noted that MWD is more specifically geared towards the drilling operation aspects and include different measurements (inclination, azimuth, downhole weight on bit, torque, …), while LWD uses logging probes (standard and azimuthal gamma ray, resistivity, density, …). In earlier implementations of MWD/LWD where communications through mud pulsing was limited to a few bits per second, LWD was recorded during the drilling operation and was processed when the drilling assembly was brought to the surface. More recent advances in data communication with the bottomhole assembly has resulted in real-time transmission of MWD/LWD to the surface (Jellison et al. 2003). LWD provides accurate measurements of the formation before it is exposed to excessive drilling fluid and its invasion, or enduring long drilling operation, and are superior to wireline logging data in that regard.