bluuebunny/crossref_metadata_2025_split huggingface dataset:
Late Middle Miocene Emergence of the Olympic Peninsula Shown by Sedimentary Provenance
Authors: Shekut Samuel , Licht Alexis 
Published: November 2020

AbstractThe Olympic Peninsula is the uplifted portion of the Cascadia accretionary wedge and forms the core of a 200 km scale oroclinal bend on the west coast of Washington State. The accretionary wedge started forming 45 million years ago following the accretion of the Siletzia igneous province along the Cascadia subduction margin. Low-temperature thermochronology studies have shown that the core of the peninsula has been continuously exhumed for the last 14 million years. The earlier onset of oroclinal bending, uplift, and emergence remains poorly documented. Here, we explore the Cenozoic drainage history of the Cascadia forearc and accretionary wedge to reconstruct the deformation history of the Olympic Peninsula. We use detrital zircon provenance and grain petrography data from modern rivers draining the Cascades, the Cascadia forearc and accretionary wedge, as well as from Eocene to late middle Miocene sedimentary units from the same areas. We first show a clear difference of sedimentary provenance between sedimentary units in the accretionary wedge, with older units reflecting mélange and imbricated strata that began as part of Siletzia, and younger units reflecting trench-fill material sourced from the Cascades and accreted to the wedge. We show that the accretionary wedge was directly fed from the Cascade arc until at least 16.5±0.5 Ma, providing a maximum age for the emergence of the Olympic Peninsula. Fluvial deposits in the Cascadia forearc basin dated at 13.3±1.3 Ma display zircon age spectra and sedimentary grain petrography features typical of recycled accretionary wedge material. Although these deposits may also reflect local input, middle Miocene exhumation rates suggest the Olympic Peninsula was an active sediment source. Our results bracket the timing of emergence of the Olympic Peninsula to a narrow window in the late middle Miocene. We suggest that the initial onset of accretionary wedge deformation and oroclinal bending predates this by at least 10 million years, in the upper Oligocene, and is marked by flexural subsidence and high sedimentation rates recorded in strata of the Seattle Basin. Our results support a composite history for the development of the Cascadia accretionary wedge rather than models predicting a gradual and steady build-up.
Tracking Condensate Banking in Tight Gas Reservoirs Through Well Test Analysis: Right Well Test Design Leads to Factual Reservoir Characterization
Authors: Mithani Aijaz Hussain , Motaei Eghbal 
Published: November 2020

Abstract
Understanding gas condensate reservoirs require comprehensive data acquisition efforts to collect representative PVT samples, measure the effective reservoir permeability and characterize reservoir behavior through well test. Right well test design is key in tight gas reservoirs to reveal all flow regimes and characterize reservoir to have valid FDP formulation including well trajectory design, well completion design and well locations and proper facility sizing.
Well test design was done and updated in appraisal to reflect condensate banking in tight reservoir with moderate condensate gas ratio (CGR). The main objective was to measure the well deliverability and reservoir characterization to get condensate banking fingerprint (signature) on pressure derivative in Modified Isochronal Test (MIT) section. This design allows to measure the permeability and radius of impaired region due to condensate banking, while extending test enough allows to measure gas zone permeability as well.
A single well radial model (SWRM) was constructed to reflect the condensate banking in simulation model as well. The log data been up-scaled to well test data in free gas zone to analyze the flow behavior of condensate banking in near-wellbore.
The condensate bank radius was estimated for different test rates in MIT test. The estimated permeability of condensate bank at different flow rates (and draw-down) are different as per expectation. This is due different saturation at different draw-down based on liquid drop-out. The permeability of single-phase gas zone (far from near-wellbore) is constant in all flow periods of MIT consistently. The results from well test is in perfect agreement with up-scaled single well model. The wellbore hydro-dynamic is modeled using transient well models (OLGA application) and showing liquid loading in long term which suggest to couple the model with transient model to consider the impact of liquid hold-up in the wellbore for proper completion design, well trajectory design and production profile forecast.
The liquid drop-out from PVT analysis was used to estimate the saturation near wellbore at each pressure and then from different draw-down periods the effective permeability of each test period correlated to this saturation. This resulted to estimate relative permeability from well test and PVT data (CVD test) which is more reliable due to extra-large scale compare to core analysis tests.
A Data-Based Approach for the Prediction of Stuck-Pipe Events in Oil Drilling Operations
Authors: Brankovic Aida , Matteucci Matteo , Restelli Marcello , Ferrarini Luca , Piroddi Luigi , Spelta Andrea , Zausa Fabrizio 
Published: November 2020

Abstract
Stuck-pipe phenomena can have disastrous effects on drilling performance, with outcomes that may range from time delays to loss of expensive machinery. In this work, we develop three indicators based on the mudlog data, which aim to detect three different physical phenomena associated to the insurgence of a sticking. In particular, two indices target respectively the detection of translational and rotational motion issues, while the third index concerns the wellbore pressure. A statistical model that relates these features with the documented stuck-pipe events is then developed using machine learning. The resulting model takes the form of a depth-based map of the risk of incurring into a stuck-pipe, updated in real time. Preliminary experimental results on the available dataset indicate that the use of the proposed model and indicators can help mitigate the stuck-pipe issue.
Absolute Permeability Prediction Model Based on Fractal Theory of Porous Media
Authors: Li Changyong , Pi Jian , He Juan , Zhang Yu , Wang Long 
Published: November 2020

Abstract
This paper reviewed many conventional capillary pressure (Pc) models, which is the foundation of estimating permeability through Pc curves. The infeasibility of Brooks-Corey Pc model for heterogeneous rocks caused poor prediction accuracy of the existing permeability model. An advanced Pc model was derived through fractal theory in which the cylindrical capillary tube was regarded as the pore unit of the fractal object. In addition, a novel permeability model was developed by substituting the integral item in Nakornthap-Evans model with the advanced Pc model expression. The permeability model established a power law relationship with the rock physical properties and characteristic parameters of Pc curves. The calibration and validation analysis were then conducted with a dataset of 210 rock samples. The fractal based Pc model can fit the Pc points measured in laboratory well, and estimate the characteristic parameters for each Pc curve through fitting work. The prediction permeability closely matches the measured permeability, which was verified by the high regression Coefficient of Determination. With the fractal dimension increasing, the absolute permeability decreases monotonically. The advanced Pc model and novel permeability model should have great application prospects in the field of Pc curve characterization and physical property analysis.
Primary Cementing Utilizing Epoxy Resins as Additive: Experimental and Application
Authors: Alanqari Khawlah Abdulaziz , Wagle Vikrant , Al-Johar Abdulwahab Sami , Al-Yami Abdullah Saleh , Ali Wajid , Abbas Athman 
Published: November 2020

Abstract
This paper discusses the designing and field application of a polymer resin-based cement system that enhances the final cement properties in terms of compressive strength and cement bond to the casing along-with minimizing the occurrence of casing-to-casing annulus (CCA) pressure during the life cycle of the well.
Several experimental studies were applied to this polymer resin-based cement system including rheology, thickening time test, free fluid test, compressive strength test, etc. The results showed that the polymer resin-based cement has improved compressive strength, ductility, and enhanced shear bond strength. Which, as a result, helps to provide a dependable barrier that would help to prevent future sustained casing pressure (SCP). Besides, the cement slurry was designed with a controlled and engineered rheology. This was done by controlling the amount of the resin and cross-linker into the cement slurry in order to achieve the target density as well as to avoid a sudden increase in the final slurry viscosity that could result in mixing difficulties at the field.
Moreover, the field deployment of this system on a 13 3/8-in casing second stage tail slurry has been discussed.
Ensemble-Based Well Location Optimization Under Subsurface Uncertainty Guided By Deep-Learning Approach To 3D Geological Feature Classification
Authors: Schulze-Riegert Ralf , Lang Philipp , Pongtepupathum Wanida , Drew Christopher , Round Andrew , Shaw Kevin , Adeyemi Tobi , Topdemir Sabahattin , Pattie Stephen , Nasiri Hamid , Hegre Thor Martin 
Published: November 2020

Abstract
Ensemble-based reservoir simulation workflows have become popular for estimating prediction uncertainty and optimizing field development objectives under uncertainty. While workflows exist for ensemble generation, ensemble analyses become an increasing challenge for extracting value from ensembles through robust field development guidance. This work presents a self-supervised deep learning modelling approach for classifying 3D-structural features applied to expert-driven field performance optimization under uncertainty.
Meaningful representations of subsurface features are essential to efficient machine learning tasks such as classification of categorical outcomes and regression for predicting dependent target variables. Unlabeled data is abundant and used for unsupervised learning which is complex in contrast to the use of labelled data. However, labelling is generally expensive and, in some cases, ill-defined or impractical due to missing modelling approaches. In this work, a self-supervised deep learning modelling approach is applied to learn semantically meaningful representations of subsurface features, e.g.,size and structure of connected volumes. The deep learning model is designed to learn a representation of 3-dimensional subsurface features. Representations are categorized, clustered and ranked for well location decision support.
For application demonstration, the self-supervised deep learning modelling approach is embedded in a structured workflow design for guiding well location selection to optimize reservoir delivery performance under subsurface uncertainty. Performance verification and application results are presented for the Olympus semi-synthetic case study. Based on probabilistic success criteria we present an optimized well placement design for which 90% of all producers deliver the economic demand at an 80% probability level or higher. In a second study the workflow is applied to a real field to discuss guidelines of use and to share expected gains in workflow efficiency, result quality and decision support.
Success Story: 64 Wells with Hybrid Real Time DTS/DAS Monitoring and Permanent Downhole Gauges Installed in the United Arab Emirates
Authors: Patel Dipen , Wee Keith , Gorgi Sam , Ashraf Hamza 
Published: November 2020

Abstract
The latest well completions developments for extended-reach wells include advanced techniques to improve the effectiveness of production and optimization solutions to achieve sustainable well production. Monitoring systems installed during the completion use real-time distributed temperature sensing (DTS), distributed acoustic sensing (DAS), and downhole gauge data to obtain better reservoir insight throughout the life of the well. This enables informed decision making to achieve optimal well production and gas injection.
Permanent downhole gauges (PDGs) with dual sensors (tubing + tubing) are installed, along with hybrid cable as part of the upper completion string. The hybrid cable has an electrical conductor and two fiber-optic lines for DTS and DAS measurements. The cable is clamped onto the tubing at each coupling and passes through the upper completion subassemblies, which include gas lift mandrels (GLMs) and tubing-retrievable safety valves (TRSVs). An intermediate completion is run before the upper completion, which comprises a two-trip permanent packer and remote actuated barrier device (multicycle).
Completions monitoring with hybrid cables provides an advantage over conventional gauge-only systems, with only a slight increase in completion costs. Using a single cable provides more run-in-hole (RIH) efficiency, fewer lines to manage, and a smaller equipment footprint. PDG data are used by production engineers, field development personnel, and subsurface personnel to determine pressure drawdown and optimize surface production choke size. Distributed fiber-sensing technology determines the effectiveness of gas lift operations to optimize injection rates, which effectively optimizes pumping rates and flow from surface. Fiber-sensing technology also helps identify tubing and annulus leaks, monitor the health of the completion during the life of the well, and minimize wellbore damage.
This type of completion has been installed successfully in 64 wells (60 production and 4 injection), and all systems are operational with data accessible from all wells.
This approach benefits the industry by highlighting best practices, providing advanced technology options for evaluating data and reservoir productivity, and providing completion and drilling effectiveness for extended deep-reach wells.
Geomechanics Recipes for Advanced Reservoir Stimulation — A Case Study for an ADNOC Field
Authors: Adigolo Charles , Ebeid Mahmoud , Eriavbe Francis , Nasreldin Gaisoni , Ni Qinglai , Mohamad-Hussein Assef , Rajaiah Nanthakumar , Subbiah Surej Kumar 
Published: November 2020

Abstract
The recent surge of interest in developing tight resources in the United Arab Emirates (UAE) has been accompanied by a growing appreciation of geomechanics challenges with potentially far-reaching implications on successfully placing and producing from stimulation treatments. The challenges are mostly posed by regional tectonics and localised ‘stress regime’ changes from normal faulting to strike-slip. Therefore, understanding the in-situ geomechanical conditions plays an important role in optimising stimulation designs and recommending safe drilling mud weights.
In this study, a 3D mechanical earth model (MEM) was built by comprehensively integrating data from multiple sources—including interpreted mechanics and lithology information such as stresses, stress direction, and core results. This 3D geomechanical model covered the target reservoir and extended from the surface to deeper formations to fully capture geological structures with high resolution. A set of faults was also incorporated to help examine their impact on well planning. Sensitivity analyses were conducted to account for uncertainties in mechanical properties and local tectonic stress state.
The 3D MEM successfully captured the observed drilling events in the thin weak inter-layers between the target reservoir for multiple wells. Some faults were shown to be critically stressed at present day. Risk of fault reactivation would increase with future production, and horizontal drilling may become increasingly difficult. The horizontal stresses in the target reservoir were significantly affected by porosity. Specifically, the low-porosity zones may have strike-slip stress regime, while high-porosity zones are in a predominantly normal faulting stress regime. The geomechanical modelling approach helped qualitatively highlight the variations in fracture containment (for a range of ‘net pressure’ thresholds), thus demonstrating the risk of breaching in a reservoir seal. It is moreover an interesting side effect that this geomechanical modelling ‘recipe’ provided invaluable insights into delineating potential ‘sweet spots’ for stimulation—essentially characterised by having high-porosity and low breakdown pressures.
The developed 3D MEM helped extend the understanding of subsurface behaviour beyond a few well locations and historic events. Moreover, the model demonstrated its predictive power in relation to identifying the potential location and pressure boundary for a future stimulation plan ultimately impacting the well placement strategies.
A Deep Autoregressive Model for Multiple Petrophysical Evaluations
Authors: Kumar Ashutosh , Priyadarshan Amit 
Published: November 2020

AbstractDeterministic methods of petrophysical evaluation is made complicated by petrophysical and geological heterogeneity within the formation even though Petrophysical logs provide most of the subsurface data to an exploration geologist. Biases between the calculated values of different petrophysical properties namely, final-porosity, horizontal-permeability, shale-volume and water-saturation, results from the use of multiple empirical formulae, assumptions and correction factors. The existing deterministic petrophysical models evaluate each petrophysical properties separately and then use empirical values to account for dependence between different properties. These models have limited flexibility to account for the existing high-dimensional non-linear dependencies between these parameters.In this paper, we propose a fully probabilistic and autoregressive deep learning model, which calculates values of final-porosity, horizontal-permeability, shale-volume and water-saturation with high accuracy in one single model instead of multiple empirical formulae. In this model, each petrophysical property is conditioned on the cumulative outcomes of the previous ones, thus evaluating all of the aforementioned petrophysical properties together.Herein, we have used raw log data from Volve, a field on the Norwegian continental shelf, which has been collected at every 0.1 m across 18 different producing and non-producing wells. The Dataset consists of raw input and corresponding multiple outputs, calculated using existing empirical equations by noted geologists. The model architecture used in this study is similar to WaveNet and consists of stack of dilated causal convolutions along with gated activations, residual connections and skip connections. Validation set, used for hyperparameter tuning and selection of best model out of all possible models, contains the properties of the entire training set. Encoders and decoders used in our model do not share parameters, allowing them to handle accumulated noise while training. When evaluated using symmetric mean absolute percentage error (SMAPE), a single deep learning model performs at par with the expert geologists. Multiple petrophysical values are calculated and were found to be remarkably accurate. Performance of the single model remains consistent across Hugin and Sleipner formation.The proposed probabilistic model models high-dimensional dependencies. It represents the information available in raw input as well as multiple calculated petrophysical values. It makes no assumption about the complex physical system, making it robust to the existing uncertainties. Proposed deep-learning model can ease the burden of repetitive and quality controlled petrophysical evaluation.
Machine Learning Based Seismic Data Enhancement Towards Overcoming Geophysical Limitations
Authors: Nakayama Shotaro , Blacquière Gerrit 
Published: November 2020

Abstract
Acquisition of complete data, i.e., unblended, well-sampled and broadband data, is technically desirable. Obviously, such a scenario is prohibitively expensive to realize. To deal with economic considerations in a seismic survey without seriously compromising data quality, we propose a machine-learning approach that offers an opportunity to acquire incomplete data, i.e., blended, sparsely-sampled and narrowband data, while still benefitting from being able to process complete data. In this study, we utilize a deep convolutional neural network. The incomplete data are fed into the applied network that simultaneously performs suppression of blending noise, reconstruction of missing traces and extrapolation of low frequencies such that prediction of the complete data is attainable. We validate the performance of the proposed method using both synthetic and field datasets. Acquisition scenarios implemented to generate incomplete datasets impose a significant reduction of data size in the frequency-space domain. Despite the limited information available in the input data, the prediction results obtained from both numerical and field data examples clearly confirm that the proposed machine-learning approach is capable of dealing with deficiencies in the incomplete data and subsequently deriving the complete data of sufficient quality. In addition to suppression of blending noise and reconstruction of missing traces, no discernible difference in prediction errors between preexisting and extrapolated frequencies is observed, which is hardly realizable with existing geophysics-based approaches. As a consequence, the proposed scheme allows for optimal data enhancement even when seismic acquisition is performed in a blended, sparsely-sampled and narrowband fashion.
Building Multifrac Completion Strategies in Tight Gas Reservoirs: A North Sea Case
Authors: Buijs Hernán , Guerra Clairet , Sonwa Roger , Nami Patrick , Vecchia Luciano , Ishmuratov Roman 
Published: November 2020

AbstractHydraulic fracture design driven by multi-disciplinary collaboration can maximize the production potential of complex multi-frac horizontal wells. Integration of multiple information sources (i.e.: geological, dynamic and geomechanical data) allows to build representative models and have proven to improve modelling towards a realistic understanding of tight reservoir performance of several multi-fracced wells. 3D properties encompassing the reservoir geological heterogeneity, pore pressure, mechanical elasticity and state of stress were utilized to develop a strategy to fracture stimulate a horizontal wellbore in the North Sea Region. The study was instrumental to build fit-for-purpose hydraulic fracture designs by incorporating state of stress changes related to pore pressure depletion on different faulted compartments supported by a reservoir dynamic simulation. Such models provided meaningful value to optimize the well trajectory used to access the host rock, understand fracture height growth possibilities in different compartments and define the number/size of hydraulic fractures required for optimum production.