bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of Salinity and Water Ions on Electrokinetic Interactions in Carbonate Reservoir Cores at Elevated Temperatures
Authors: Ayirala Subhash C. , Saleh Salah H. , Enezi Sultan M. , Al-Yousef Ali A. 
Published: February 2018

Summary
Smartwater flooding through tailoring of injection-water salinity and ionic composition is becoming an attractive proposition for improved oil recovery in carbonate reservoirs. Most recent studies suggest that surface-charge change induced by lower salinity and certain water ions on carbonate surfaces is the main mechanism responsible for favorable wettability alteration and, consequently, higher oil recovery in smartwater flooding. Unfortunately, these studies determined ζ-potentials using the technique of electrophoretic-mobility (EPM) measurement with powdered-crushed-core samples, which may not reflect the natural conditions existing in the subsurface reservoirs. In this study we used an experimental technique dependent on streaming-potential (SP) measurements to determine ζ-potentials in intact carbonate reservoir core samples saturated with different brine salinities and individual ion compositions at both ambient and elevated temperatures.
The results indicated a favorable effect of sulfate ions in carbonate reservoir cores to alter ζ-potentials toward more negative, and the reactivity of these ions increased significantly by nearly one order of magnitude at higher temperatures. Among the positive ions, calcium showed the highest reactivity to shift the ζ-potentials to slightly positive. Both magnesium and sodium ions showed almost similar behavior to change the ζ-potentials toward less negative. In addition, only minor to moderate changes in ζ-potentials were observed with the positive ions when the temperature is increased. In view of considering the reported negative ζ-potentials at the carbonate crude-oil/brine interfaces, such a change in ζ-potential to an extreme negative obtained in carbonate reservoir cores upon exposure to injection waters containing sulfates could have a favorable effect on wettability alteration because of the resulting same polarity at both the interfaces. The dynamic time-dependent effects on ζ-potentials measured during the displacement of seawater by smartwater (10-times-reduced ionic strength seawater) in reservoir cores showed an immediate shift in the ζ-potential value from positive to negative. This instantaneous change to negative values observed in the ζ-potentials confirms the beneficial effect of smartwater to activate favorable electrokinetic interactions in carbonate reservoir cores.
A Semianalytical Method for Modeling Two-Phase Flow in Coalbed-Methane Reservoirs With Complex Fracture Networks
Authors: Yang Ruiyue , Huang Zhongwei , Yu Wei , Lashgari Hamid R. , Sepehrnoori Kamy 
Published: February 2018

Summary
Coalbed-methane (CBM) reservoirs are naturally fractured formations with cleats surrounding the coal matrix. Analyzing and predicting CBM-production performance is challenging, especially for early-time production, because of the complex fracture networks and gas/water two-phase flow.
In this study, we develop an efficient semianalytical model to predict gas and water production in CBM reservoirs with multiscale fracture networks. The activated large-scale or interconnected cleats and hydraulic fractures are modeled explicitly as discretized segments with connected nodes. The small-scale cleats and disconnected natural fractures are described implicitly as “enhanced matrix permeability.” We incorporate critical gas-flow mechanisms and stress sensitivity of the fracture network in the model. The two-phase-flow mechanism is considered by iteratively correcting the relative permeability to gas/water for each fracture segment and capillary pressure at each node with the reservoir depletion. We verified the model against a numerical reservoir simulator, field data, and an analytical solution. Subsequently, we apply the model to quantify the effects of fracture-network complexity/connectivity and stress sensitivity on gas/water-production behavior.
This work presents an accurate and fast semianalytical model to perform two-phase flow of gas and water in CBM wells with complex fracture networks. The approach is easier to set up and less data-intensive than using a numerical reservoir simulator, and more flexible in handling the complex-fracture networks than full analytical models. This method provides a promising technique for better understanding the effect of the cleats and fracture networks present in CBM reservoirs on gas and water production.
Using Simulator to Prepare for Total Loss Risk Scenarios Utilizing Controlled Mud Cap Drilling in the Barents-Sea
Authors: Oedegaard S. I. , Hollman L. , Smaaskjaer G. , Claudey E. , Mehus Ø. , Andreassen T. , Nabavi J. 
Published: February 2018

Abstract
The objective was to be prepared for a total and sudden loss scenario while drilling and coring a challenging well in the Barents sea. A dual-gradient Controlled Mud Level (CML) system with Controlled Mud Cap Drilling (CMCD) mode was installed on the rig to manage minor and/or total losses. Prior to spud of the section, an advanced dynamic simulator with the actual well configuration loaded was used to conduct offline training, and prepare the drilling team and involved service personnel for the operation.
Experience from previous wells in the area identified the risk of drilling into karstified carbonate zones with the potential of leading to total and sudden losses. An advanced dynamic simulator was used to reflect the details of the CML system to be used. The rig crew together with the CML operator and other involved service personnel were trained on how to manage a total loss scenario by switching from CML to CMCD mode. All relevant operational procedures were used as a basis for creating training scenarios and operational preparations for the exercises.
This paper will briefly present the simulator set-up, the operation/training procedures and results from the training. Feedback from the operation itself will also be described including lesson-learned from utilizing a full-scale dynamic simulator with the actual well loaded during preparation for operation.
A Practical Model for Wellbore Strengthening
Authors: Chellappah Kuhan , Majidi Reza , Aston Mark , Cook John 
Published: February 2018

Abstract
This paper presents a new model that integrates relevant aspects of geomechanics and drilling fluids to provide practical solutions for wellbore strengthening. This integrated approach accounts for the influence of stress state, rock properties, wellbore design parameters, and the impact of particle-laden fluids on the creation, growth, and arrest of an induced fracture. A dimensionless parameter called the Wellbore Strengthening Index (WSI) is introduced as a measure of desired strengthening normalised to the rock stiffness. WSI is used to estimate the concentration of wellbore strengthening material (WSM) required for a given application. A semi-empirical correlation then relates solids loading to the length of an induced fracture at time of sealing. A closed-form equation is proposed to estimate fracture width using this calculated fracture length. Once induced fracture dimensions are determined, appropriately sized WSM is selected for efficient sealing. The model is relatively straightforward to code into a practical and user-friendly design tool for wellbore strengthening. Two case studies are presented to show successful implementation of the new model.
Fuel Economy and Emission Characteristics of a High Horsepower Natural Gas/Diesel Dual-Fuel Engine in Oil & Gas Operations
Authors: Wijesinghe Asanga , LaFleur Carolyn , Meng Fanxu , Colvin John , Haut Richard 
Published: February 2018

Abstract
Oil & gas industry continuously seeks alternative ways to combine cleaner and lower cost gaseous fuel with diesel fuel as a diesel fuel supplement to reduce undesirable emissions and expensive fuel cost from these diesel engines. One substantially increasing technology is converting CI diesel engines to natural gas/diesel dual-fuel engines using a moderate engine modification. However, just how these modified engines perform in drilling operations is not well documented and require further assessment. Using advanced instrumentation designed for use at active drilling sites, fuel consumption, gaseous emissions, and soot in exhaust from a high-horsepower engine coupled to a generator were measured in both dual-fuel and diesel-only operation modes. Dual-fuel engines have the advantages of providing the same power as conventional diesel engines, producing lower amounts of emissions such as nitrogen oxides and soot, and cost saving through diesel displacement up to 50 %. In addition, the results show that the emissions from combustion of natural gas are very different from those of the diesel fuel it displaces. Co-combustion of the two fuels in a CI engine exhibits distinct emissions characteristics, with advantages in reduced oxides of nitrogen, carbon dioxide, and soot. Significant disadvantages were seen in emissions of non-combusted methane and formaldehyde. In some conditions, as much as 30% of the gas fuel was emitted unburned, significantly diminishing fuel efficiency and increasing greenhouse gas emission.
Recorded Well Data Enriches the Testing of Automation Systems by Using a Deep Neural Network Approach
Authors: Yu Yingwei , Chambon Sylvain , Liu Qiuhua , Belaskie James Philip 
Published: February 2018

Abstract
One technique useful in the testing and development of drilling automation system is to use synthetic data. A good drilling time series simulator can enrich a dataset for testing, and enable the inference of the states of drilling in real time. However, conventional simulators do not generate the "warts" of real data (noise, gaps, etc.). The proposed solution is a model that learns from real data, characterizes the different drilling responses and conditions the data with a deep neural network (DNN) approach, and generate realistic drilling time series dataset.
To simulate a drilling-time series dataset, a DNN can model physical properties of the formation, rig, and sensors, and generates data with realistic curve patterns when it is trained on actual measurements, e.g. block position, hook load, standpipe pressure, and surface torque. The neural network has multiple convolutional, recurrent, and fully-connected layers. The model, trained with wellsite recorded data, captures the spatio-temporal distributions among data channels, and then uses a windowed input to predict the next data points, which are then fed back into the network to generate the simulated data sequence recursively. An actual sensor drilling-time series dataset containing various channels are input into the DNN. The networks contain eight convolutional layers with three max-pooling layers, three recurrent layers, and four fully-connected layers. The time window used in the input contains 512 samples for each channel, while the output is 1 sample for each channel.
After training the network for 200 epochs, the network can successfully simulate time series data recursively. The simulated time series preserve the features of the original training data, while maintaining the data distribution of multiple channels. For example, the network shows a consistent "inslips" pattern in the hook load channel when the block position moves quickly from bottom to top. Currently the simulation is autonomous based on the training data, and does not take input as controls, which is our future steps.
The proposed DNN model is a low-cost, robust model that simulate drilling-time series datasets containing complex spatio-temporal patterns. Our proposed algorithm is the first known simulator of drilling time series datasets that models the nontrivial physics laws and properties, including formation, rig, and sensors, and generates data containing realistic curve patterns with a deep neural network approach. The simulator greatly helps the inference component of automation systems with the enrichment of datasets that are available for testing.
Wellbore Stability Solution Incorporating the Weak Bedding Planes Effect with Field Case Study
Authors: Mehrabian Amin , Pérez Arturo Díaz , Santana Claudia 
Published: February 2018

Abstract
Wellbore stability in shale is often hampered by the detrimental effect of existing weak bedding planes on the strength of the rock surrounding the wellbore against shear failure. This paper presents results from a formal closed-form analytical solution to the wellbore stress problem that incorporates rock failure along weak bedding planes. The solution is used for a case study of a highly inclined well section in a laminated layer of troublesome shale with a strike-slip faulting regime above the target formation in the Latin America region.
Real-Time Bit Wear Monitoring and Prediction Using Surface Mechanics Data Analytics: A Step Toward Digitization Through Agile Development
Authors: Liu Yu , Kibbey Justin , Bai Yanbin , Wu Xianping 
Published: February 2018

Abstract
Severe bit damage is a known issue in west Texas land drilling due to abrasive sand formation and interbedded hard stringers. Operational performance and rig cost are often impacted by penalty fee of bit DBR (damage beyond repairable), low ROP (rate of penetration) with worn bit, and inefficient decision-making on tripping. A real-time data analytics application is developed aiming to provide actionable information to operation to expedite decision making process.
A historical dataset of surface mechanics data and bit records is collected from 40 bit runs drilled in 2016 and early 2017. A hybrid data analytics procedure consisting of conventional physical modeling of drilling mechanics and supervised learning using machine learning technique is conducted to separate bit failure pattern from normal formation transition and drilling parameters adjustment. A metric based algorithm is constructed for real-time monitoring of bit drilling performance and early warning on bit cutter wear conditions.
A web-based real-time software is developed and field trialed on three wells with satisfactory results. Subsequent deployments in DART (Drilling Automation Remote Technology) center and field offices have been quickly rolled out for five rigs in west Texas. Positive feedback is generated from operation and engineers. Attributed to the success of agile development framework and adaptive software architecture, other advisory mode features such as motor life monitoring, smart-tripping evaluation, and sliding diagnosis etc. are under development.
The application discussed in this paper combines expert's domain knowledge with machine learning techniques and provides actionable information to support on-site operational decisions. The development and deployment of this application follows an agile mode innovation framework, through which operational need and technical solution are quickly bridged and tangible business value is able to be delivered in short term.
The Role of Big Data in Operational Excellence and Real Time Fleet Performance Management—The Key to Deepwater Thriving in a Low-Cost Oil Environment
Authors: Bolen Matt , Crkvenjakov Vladimir , Converset Julien 
Published: February 2018

Abstract
Over the past three years, the oil and gas industry has experienced its deepest downturn since the 1980s. Recovery has been slow, setting the deepwater industry at a strategic inflection point where step changes are necessary to remain competitive. Considering that deepwater upstream capital projects are some of the most expensive projects in the industry, capital costs on a per well basis must be reduced in order for deepwater to continue to attract capital. Achieving operational excellence by embracing the big data revolution will help answer the challenge in thriving in a low-cost oil environment.
Oil and gas operators as well as others suffer from data overload. A major challenge in the process of achieving operational excellence is to find a way harness big data and capitalize on its benefits. The paper outlines the solution that a major operator and service company developed that established a unique well construction optimization process, improved consistency and moved every operation closer to the technical limit.
The solution was developed for the major operator in Gulf of Mexico (GOM) deepwater fleet and was successful in reducing the well cycle times by: Applying a unique approach and workflow to transform big data into usable knowledge and enable critical thinking in the process of data analysis. This led to challenging current established operational procedures, making adjustments to actual well conditions and maximizing the efficiency of the rig.Enhancing a standard set of key performance indicators (KPIs) so that each operation is measured and the performance is understood.Establishing an effective in-house real-time data analysis center that fully supports the integrated drilling teams in order to drive data-driven decision making.Utilizing concise visualization of surface digital data so that clarity from the data can be gained and insights communicated to the rig teams.
Well Integrity: Coupling Data-Driven and Physics of Failure Methods
Authors: Das Bibek , Samuel Robello 
Published: February 2018

Abstract
A hybrid model based on Physics of failure and Data-driven algorithms is developed that can estimate remaining useful life of production casing (well barrier). The state of integrity of the well barrier is assessed by updating the reliability under operational loads.
The interactions between the casing and surrounding formation, and effects of tribocorrosion on the casing are considered. Tribocorrosion is the process of degradation of a material resulting from a sequential process of (i) mechanical wear (due to sliding, friction, or impact) followed by (ii) a corrosive action of the surrounding environment. The model includes simulating casing wear due to drilling, and enhanced degradation due to conditions in the well.
The main capability of the model is to help well integrity analyst with insight of future health states of a monitored well. This is achieved in two main steps; the first being the offline module comprised of degradation models. The second is the pattern recognition based on well log and features mapping, and estimation of remaining useful life of well barrier. The production casing grade P-110 undergo reduction in strength due to wear during drilling, induced stress and hydrogen induced cracking. The remaining useful life is calculated for the depths of interest and time.
A comparative analysis is carried out using the industry standard soft-string model versus a more comprehensive stiff-string model to estimate wear. The paper presents a unique approach to predict the remaining useful life of a well barrier and the dynamic state of the well's operational integrity. The prediction is not solely based on statistical modeling but also incorporates barrier engineering and physics of failure in the model.
Gas Kicks in Non-Aqueous Drilling Fluids: A Well Control Challenge
Authors: Ma Z. , Vajargah A. Karimi , Chen D. , van Oort E. , May R. , MacPherson J. D. , Becker G. , Curry D. 
Published: February 2018

Abstract
Non-aqueous drilling fluids (such as synthetic-based mud) are frequently used to drill one or more sections of an oil/gas well to reduce drilling problems such as shale sloughing, wellbore stability, and stuck pipe. However, solubility of formation gas in such fluids makes early gas detection and thereby the well control process very challenging. This is of particular concern in deep offshore wells, in which large amount of gas can be dissolved in non-aqueous drilling fluids under high pressure and temperature conditions. The gas remains in solution until the bubble point is reached. Thereafter, a sudden release of gas at shallow depth can compromise wellbore and riser integrity, particularly when the gas has passed the blow out preventer installed at the seafloor. An advanced planning tool to simulate the transient multi-phase phenomena associated with gas kicks in non-aqueous drilling fluids is therefore highly desirable.
This paper presents a novel and comprehensive hydraulic model with associated calculation routines and software to simulate a gas kick in non-aqueous drilling fluids. A transient drift-flux approach based on conservation of mass and momentum was applied in association with appropriate algebraic closure equations and sophisticated friction and choke models. Advanced numerical schemes, where applied previously, have been modified to handle the mass transfer between the liquid (mud) and gas phases. In addition, PVT models have been included to investigate and predict the effect of gas solubility in various types of drilling fluids.
The calculation routines contained in a new software tool predict crucial parameters during well construction such as pit gain, gas break out location and void fraction, annular pressure profile, kick tolerance, choke opening, flow-out, standpipe and casing pressures. Simulation results generated using the tool are presented here for both water-based and synthetic-based muds to illustrate the impact of gas solubility on kick behavior. The tool can handle several other complexities which occur during a well control incident such as multiple influxes from one or several formations, dynamic well control (suitable for managed pressure drilling), automated choke control, sudden pump startup/shutdown, non-Newtonian drilling fluids, arbitrary wellbore path, lost circulation, etc.
Applying advanced numerical schemes associated with relevant PVT models and several types of boundary conditions makes the tool comprehensive, unique, robust, and efficient for well control analysis for a variety of complex drilling scenarios, particularly deepwater wells. As such, it has the potential to enhance well control operations and well design, thereby enhancing rig safety and reducing non-productive time and cost associated with well control-related events.