bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Resource-Oriented Method of Oily Sludge Disposal in ASP Flooding Production
Authors: Wang Zhihua , Rui Zhenhua , Lin Xinyu , Xu Mengmeng , Bai Ye , Luo Jiaqi 
Published: October 2017

Abstract
In the field practice of alkaline/surfactant/polymer (ASP) flooding, there is more oily sludge produced and accumulated in settling tank, dehydrater, recycling pool, etc. Timely and effective disposal of oily sludge has already played an indispensable role in establishing the surface and subsurface integration pattern with chemicals EOR. Many conventional disposal methods faced to the challenges of separation efficiency, facilities pollution, healthy and environmental threats, and economical justification with the appearance of alkali, surfactant and polymer in produced liquid (mixture of oil and water of wells). We focus on using physical comprehensive effects to remove the emulsified oil and absorbed water, and the in-situ utilization of the left sludge was regarded as a method development to address the challenges recently. A laboratory investigation of characterizing the properties of oily sludge in ASP flooding production was recently carried out. The heating-washing-centrifuging combination treatment process was practiced. After removing its emulsified oil and absorbed water, the sludge transported to filter bed to form a suspended sludge blanket was studied and the feasibility of in-situ utilization of filtering separated water was also evaluated. The application effects of the resource-oriented disposal method were assessed, and the integration technology and operation parameters were designed and optimized.
The results indicate that the oily sludge with ASP flooding is characterized by high caloric value, high water cut, high viscosity, strong electronegativity and stability, and there is relatively high melting temperature, more wax and asphaltenes in oil phase. There appears to be a linear relationship between the scale of oily sludge deposition in surface facilities and the appearance concentration of chemicals in produced liquid of production wells. Compared with the previous thermochemistry demulsification disposal methods, dirty oil recovery ratio increased more than 15%, and running expense was reduced around 20%. The dirty oil content in the sludge was less than 2% when disposed under the combination disposal process and operation parameters. Furthermore, the formed suspended sludge blanket has the functions of in-situ purification of separated water, achieving the cyclic utilization in washing process. The probable impacts of its direct discharge into the normal sewage treatment system reduced and secondary pollution decreased spontaneously. This study is beneficial to provide a robust and potential way for disposing oily sludge in the production and operation of chemicals EOR, and it is also significant to understand the surface and subsurface integration idea and further accelerating ASP flooding application in high water cut oilfields.
Novel Application of Elemental Aluminum and Zinc to Generate aSelf-Compressing Silicate System for Use in Lost Circulation and Conformance
Authors: Soucy Kelly , Cramer Brett , McDonald Michael , Li Xianglian 
Published: October 2017

Abstract
Effective zonal isolation is critical in lost circulation, cement repair and conformance applications. To be successful it is often necessary to not only block pathways but also ensure a tight seal. Gaps or weak points between the blocking material and boundary layers can result in poor zonal isolation leading to gas migration, gas entrapment, and/or excessive water production among other issues. The mechanisms vary but cement, polymers, and sodium silicate can all lose volume upon setting and aging. The degree of contraction being impacted by downhole events such as fluid loss, influx of gas, water or conditions such as high temperatures
In the case of Portland cement, several different methods have been developed to ensure dimensional stability. A long time approach has been the addition of aluminum powder to the cement slurry for the in-situ generation of hydrogen gas. This paper looks at how elemental aluminum as well as zinc can be adapted for use in a sodium silicate-based system. In developing a new technology, several questions are posed at the onset. At the top of this list are the health, safety, and environmental implications of the individual components and final product. Fine powders are inherently dusty and carry an explosive risk if not properly handled. Development of a safe form of the metal powders became the first priority. The direction taken was to slurry the metals using suitable base oils and mutual solvents. As part of the slurry development, the shape and size of the aluminum and zinc were studied for resistance to settling, rheological stability, and reaction kinetics.
Stable metal slurries could be formed in base oils such as polyalphaolefins or mineral oils when combined with other additives. The selected base oils were shown to function in a similar manner as encapsulators and be used control the rate of the gas generation reaction. Mutual solvents such as triacetin provided further functionality by being able to initiate a polymerization reaction of alkali silicate. Short-term properties such as set time, density, rheology, and compressive strength were made adjustable by concentration and the use of filler/bridging material such as walnut hulls, glass powder, calcium carbonate, barite, or fly ash.
While sodium silicates can be set by either an internal or external setting agent, the industry preference is for a one component system. Presented in this paper will be the development of a one component system that safely incorporates the in-situ generation of hydrogen to yield a chemically durable, mechanically strong expanded silicate-based plug.
Streamline Rock Facies Classification with Deep Learning Cognitive Process
Authors: Mohamed Sidahmed , Roy Atish , Sayed Anjum 
Published: October 2017

Abstract
Hydrocarbon resource identification process involves significant endeavors and persistent efforts to identify sweet spots and economical discoveries. To help streamline the process and better support subsurface teams, a suite of machine learning models have been piloted for integration into the workflow. This paper introduces a sub-process of classification models for automatic prediction of reservoir rock facies types based on acquired sample well logs.
We present results from a synthesized dataset based on a formation with a series of different stacked reservoir rock types. The main reservoir is further sub-divided into four sections.
Our method is based on cognitive deep structures, where more complex facies attributes are inferred from lower level raw features. The algorithm mimics biological brain hierarchical order for recognizing patterns/objects. The incremental learning process iteratively improves on facies identification as new evidence is introduced to the machine learner. We train the model on a small sample of well log attributes and validate performance on excluded wells.
The model performs relatively well on a limited training sample. In a typical field development process, only a few appraisal wells are available for explorers to determine resources location and juxtaposition. Results show that distinction between good quality homogenous to parallel or cross-bedded rock from poor quality clay rich laminated facies is well predicted. This is an important step forward, as it helps geologists and geophysicists re-affirm conclusions about best, good, and poor quality rock type locations. The integration of deep learning models into the appraisal process provides an additional assessment lens and helps accelerate full field development of the resource.
This paper introduces an enhanced capability for intelligent classification of reservoir facies based on well log attributes. The cognitive deep learning sub-system has the advantage of self-adaptability as more samples become available. Our approach yield superior results relative to the best outcome of an industry collaborative facies prediction using machine learning, and is a demonstration of the potential of emerging digital capabilities for Upstream workflows.
A Novel Networked Check Point System Keeps Track of Human and Vehicle Flow in and out of Oilfield Locations
Authors: Haci Marc , Quezada Arturo 
Published: October 2017

Abstract
A new networked check point tower system has been designed specifically for remote oilfield work sites. The system keeps track of human and vehicle flow providing an accurate headcount of who is on location at any given time. The check point tower enhances operational safety and aids in enforcing compliance with company policies.
Automated and networked check point tower(s) can watch over an area of interest and allow workers to check-in and check-out with their unique remote RF devices. The new approach is a considerable step up from sometimes employed manned gate guards with a clipboard or a sign-up sheet at the rig manager's door. A set of cameras and sensors are packaged in a light, portable tower that can quickly move with the drilling rigs or completion crews or be deployed on unattended locations. Towers upload events data continuously to a central server and such data is monitored 24/7/365 and available to clients as interactive analytics reports. Flexibility allows deployment of a single tower or multiple towers in fields of varied complexity and synchronized to account for traffic entry and exit from multiple directions.
Development and design criteria of the check point towers are explained in this paper. The system of towers is easily deployed in attended drilling and completion locations as well as unattended production locations. Case histories show accurate personnel head count positively impacts operational safety. Presence of check points discourage theft and encourage personnel productivity in unsupervised locations.
This paper introduces a novel tool that just become available in the oilfields that finally enhances counts and records who is on location decades after the industry begun to add to the digital oilfield hundreds of operational parameters such as pressure, flow rate, block height, or trip speed.
Phase Behavior Change Due to Rock Deformation in Shale Reservoirs: A Compositional Modeling Approach
Authors: Uzun Ilkay , Eker Erdinc , Kazemi Hossein , Rutledge Jeffrey M. 
Published: October 2017

Abstract
Pore diameters for shale reservoirs are on the order of few nanometers which become even smaller during production because of rock deformation. This dynamic interaction between pore fluid pressure and rock stress affects the phase behavior in unconventional reservoirs. In this paper, a new mathematical formulation of fully-coupled geomechanics and compositional dual-porosity model was used to determine the impact of rock deformation and confinement on the nanopore fluids as well as their effect on the production performance of Eagle Ford formation. The formulation presented was derived from our multiphase poroelasticity model which was an extension to the single-phase, single-porosity Biot's linear poroelasticity theory allowing to characterize the rock deformation and pore diameter reduction using the bulk modulus of the matrix-fracture system.
Changes in reservoir pore pressure and rock deformation that cause the pore diameter to reduce increases the capillary pressure in the pores which affects the bubble-point pressure suppression and significant shift in the phase envelope, favoring longer period of single-phase production. It was observed that not taking rock deformation into account will lead to over estimation of production, whereas ignoring the effect of pore confinement would underestimate the production forecast. In an example field study based on Eagle Ford reservoir, an increase of around eight percent in cumulative oil production was achieved when the effect of rock deformation and confinement was included in the compositional model compared to the case where only the rock deformation was included. On the other hand, if only pore confinement effect was included in the simulation runs, four percent of increase was achieved.
Real-Time Bit Wear Prediction Using Adaptive Data Analytics
Authors: Lakhanpal Vikrant , Samuel Robello 
Published: October 2017

Abstract
As the oil and gas well geometry becomes more complex, identifying the locations of critical bit wear becomes increasingly difficult. The analytical models currently used in the drilling industry are incapable of completely realizing the downhole dynamics and thus, are incapable of bit wear prediction. Current models either overestimate or are too conservative, while some models necessitate too many parameters, constants, and coefficients. There is no acceptable universal mathematical model that describes bit wear; thus, this paper proposes a method that does not use a model at all. This study focuses on harnessing the impact of real-time downhole dynamics on drilling parameters to predict bit wear using adaptive data analysis techniques.
This paper identifies a parameter that demonstrates the impact of downhole dynamics with a high degree of functionality. Though other parameters can also be used, real-time rate of penetration (ROP) is the basic parameter always available. Depending on the parameters available, different parameters can be combined and entered into the algorithm. For example, ROP and gamma ray data can be convoluted in an appropriate manner to predict bit wear and whether the bit wear is caused by formation change.
This work proposes an adaptive data analytics algorithm. This algorithm decomposes the continuously entered incremental ROP data that come in the form of wellsite information transfer standard markup language (WITSML), at each depth into its intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). The trend of the final IMF at each depth is continuously monitored to predict bit wear. For the practical purpose of making the entire process fast and straightforward, the energy of the final IMF (IMF energy) is also continuously computed. This algorithm can be implemented using incremental depth analysis and incremental depth interval analysis. The depth interval analysis can be used during the preplanning stage of the drilling operations to identify the intervals in which the field personnel should be more cautious. The incremental depth analysis can be used during drilling operations.
The proposed workflow was tested on historical wells, and the results were compared with analytical models [Rashidi et al. 2010 (more conservative) and Liu et al. 2014] used in the industry. The results were also verified using the daily drilling reports (DDRs) from the field to confirm the algorithm predictions. The predicted results were synchronous. A warning was generated well before the actual failure caused by bit wear occurred. There were no false results recorded.
Challenges and Lessons from Implementing a Real-Time Drilling Advisory System
Authors: Spivey Benjamin J. , Payette Gregory S. , Wang Lei , Bailey Jeffrey R. , Sanderson Derek , Lai Stephen W. , Charkhand Behtash , Eddy Aaron 
Published: October 2017

Abstract
This paper discusses the technical challenges related to implementing a rig-site, real-time drilling advisory system and current solutions to these challenges. The system uses a data-driven response surface model based on physics-based calculations to optimize rate of penetration (ROP) while minimizing drilling vibration dysfunction with regards to lateral (whirl) and torsional (stick-slip) vibrational modes. Minimizing these vibrations is important to mitigate bit damage that can lead to reduced ROP and bit trips. By incorporating drilling vibrations with ROP optimization, the system helps operations identify drilling limiters and support the bit and BHA redesign process to improve drilling on subsequent wells.
Throughout development, the team has identified and managed challenges related to estimating drilling performance from data, making optimization trade-offs, guiding the driller to characterize performance, and handling formation variability. Surface sensors provide real-time drilling data measurements to an electronic drilling recorder which converts data into Wellsite Information Transfer Specification (WITS) records. These measurements often require filtering, averaging, and transformation with physical models to estimate drilling performance with suitable accuracy for driller guidance. Improving drilling performance involves a tradeoff between optimizing ROP and avoiding drilling dysfunction. The system currently uses a drilling efficiency term, such as mechanical specific energy (MSE),to incorporate whirl and other energy losses, and it computes an absolute estimate of bit stick-slip with the torsional severity estimate (TSE). This drilling optimization tradeoff requires comparing relative measures of performance, such as ROP and MSE, with an absolute measure of performance as TSE. A drill-off test involving changing the drilling parameters and observing performance is central to create performance trends versus the drilling parameters. A method for guiding the driller to conduct a drill-off test should accommodate driller human factors, capture sufficient data to provide an accurate trend, and complete the drill-off test in a sufficient amount of time to use the test results to optimize the current formation. Data-driven trends should be relevant for the formation the bit is currently drilling. Formation changes may occur with varying magnitude and frequency, and the system addresses aspects of formation change.
This paper discusses these drilling challenges using field and simulation data and provides results from recent North America land operations targeting unconventional oil and gasdevelopments. Field feedback has been incorporated for continuous improvement to algorithms. This deployment demonstrates the benefit of using the rig-site system for real-time drilling set point decisions and post-well drillstring redesign decisions.
Subsea Spares Analysis: Using Reliability, Production Impact and Cost Data to Deliver Optimum Value
Authors: Bonini C. , Clarke Y. 
Published: October 2017

Abstract
Subsea spares are expensive to procure and store; however, the potential consequences of having spares unavailable when required can be even worse. The main objective of this study is to create a tool to assist with the evaluation of the optimal number of spares for each subsea equipment, considering item cost, maintenance requirements, lead time and production impact.
The first step of the spares analysis process is to understand how reliable/unreliable the equipment is. This is carried out by following FMEA (failure modes and effects analysis), i.e. identifying failure modes, failure frequencies, failure consequences and repair scenarios (such as mean time to repair) based on asset specific performance data and industry historical data. The maximum number of spares depends on the cost associated with maintaining that item in storage and performing preventive maintenance. The minimum number of spares is determined by probabilistic analysis, based on Poisson distribution functions.
A Spares Analysis Tool was developed: it uses Poisson probability distribution and a cost-benefit analysis to calculate the ideal number of spares. This Excel based tool allows the subsea team working on the Frade project to be able to quickly evaluate the proper stock level of key components and ensure continuity of operations in a reliable and cost efficient manner. It also allows adjustments to be made according to changes in the oil price (which affects the criticality of the spare in terms of total Lost Production Opportunity, or LPO), and in case changes in failure rate are observed for a specific component over a long period of time. Failure rates can vary significantly depending on equipment operating conditions, and because of that, industry historical data may not always reflect the reality of every single project.
The results obtained by using this tool also assist with communicating with management and justifying expenditures in a scenario where costs need to be reduced.
No previous information related to the development of a tool to determine the optimal number of spares was found. In the current oil price environment, this tool can bring an important contribution by assisting teams to make informed decisions based on statistical information and field-specific failure data.
The strategy provides a reliability and risk based approach in the decision-making process.
The Study on Expandable Tubulars Collapse Performance and the Development of Risk Based Design Collapse Strength
Authors: Long X. , Roggeband S. M. , Pasaribu H. , Jabs M. , Klever F. J. 
Published: October 2017

Abstract
Solid Expandable Technology (SET) is helping the industry to reach more challenging wells. The objective of this study is to comprehensively investigate the effects of the expansion process on the pipe mechanical properties and collapse performance, and to develop a risk based design collapse strength for the expandables with a desired reliability level.
Full size expanded and unexpanded pipes with various D/t ratios were collapse tested. Detailed material properties were measured, such as large deformation material tension and compression test, X-Ray diffraction residual stresses measurement and detailed pipe geometry and wall thickness mapping. An FEA model was also developed to simulate the collapse performance of the expanded tubulars. A power law fitting formula and the Klever-Tamano formula were used to obtain the best fit mean (average) collapse strength of the test data. Then a risk based statistical approach was employed to develop the design collapse strength.
It was found that the expanded tubulars generally have 10-30% lower collapse performance than unexpanded tubulars depending on the pipe D/t ratios. This is due largely to the Bauschinger effect of the material caused by the expansion process. Other factors, such as pipe residual stresses development, and pipe geometry variations may play a secondary role. Traditional API collapse formulas may not be sufficient for the design collapse strength determination of expanded tubulars. A statistical approach was successfully employed to develop the collapse strength for expanded tubulars at 99.5% reliability level. Thus it leads to a reliable and safe application of expandable tubulars in well design and drilling operations.