bluuebunny/crossref_metadata_2025_split huggingface dataset:
Real Time Stuck Pipe Prediction by Using a Combination of Physics-Based Model and Data Analytics Approach
Authors: Zhang Feifei , Islam Aminul , Zeng Hao , Chen Zengwei , Zeng Yijin , Wang Xi , Li Siyang 
Published: November 2019

Abstract
Nearly one third of the drilling time lost is caused by stuck drill pipe. In many cases, stuck pipe is preventable if early signs are detected and timely measures are taken, particularly for stuck pipe events caused by solids (primarily drilled cuttings) in the wellbore. This paper presents a physics-based model and data analytics combined approach to predict stuck pipe caused during drilling.
The new proposed method combines the physics-based first principle models, including transient solid transport model, drill string model (torque and drag model) and the data-driven models. The proposed models will be worked based on the analysis of both field and experimental data. The physics-based models capture the basic rules of fluid mechanics, drill string mechanics, and multiphase flow during the drilling operations.
By analyzing field data from historical wells and experimental data, an EnKF based data-driven model is applied to provide parameters and coefficients needed by the physics-based models. The data-driven model improves the reliability of the results predicted by the first-principle based model and allows it to continuously improve itself.
Based on a transient approach, this development can use real-time drilling operational data as inputs, predict the stuck pipe risks, and provide warnings when a high risk for stuck pipe scenarios are encountered. Comparing to existing stuck pipe prediction approaches, the new proposed approach can distinguish the hole cleaning related stuck pipe risk and other reasons to create stuck pipe. This hybrid method in build technology will use as a supporting tool in decision make. Resulting to bring an opportunity for the drillers to avoid the potential stuck pipe incidents by taking a proper action in time.
Acid Gas Removal Unit Successful Switch from Silicon to Polyglycol Antifoam to Eliminate Foaming
Authors: Al Rumaih Mohammed 
Published: November 2019

Abstract
Natural Gas Liquefaction (NGL) plant Acid Gas Removal (AGR) unit successfully switched from silicon based to polyglycol based antifoam. Changing the type of antifoam was to eliminate the repetitive foaming occurrences in the AGR amine absorber, and to avoid the side effects of the excessive silicon deposits in the circulated amine. The polyglycol based antifoam was found to be more effective and required at lower concentration.
The AGR unit experienced repetitive amine absorber foaming since the plant commissioning. The foaming events are due to feed quality challenges in the form of liquid hydrocarbon carryover, and suspended solids entrainment. The repetitive foaming led to frequently disturbing the plant operation, reducing the unit throughput and impacting the product quality. To decrease the foaming occurrences the mode of operation for the antifoam injection was changed from intermittent injection to continues injection. However, the unit was so sensitive to the antifoam flow interruptions.
The required concentration of the silicon based antifoam was high (110 – 130 PPM compared to 20-25 PPM for the polyglycol based antifoam). This led to the side effects of the excessive silicon deposits in the system, such as the fouling of heating equipment (heat exchangers, reboilers and air coolers), the frequent amine filters clogging, and increasing the circulated amine total suspended solids.
The main objective for switching the type of antifoam is to eliminate the foaming events while minimizing the side effects of the silicon deposits. The polyglycol based antifoam was found more effective for continuous injection with concentration of 20-25 PPM. Furthermore, in the case of minor foaming in the amine absorber, increasing the antifoam concentration to 500 PPM was found to be effective in stabilizing the absorber foaming in less than 5 minutes. In addition, the minor antifoam flow interruptions did not result in foaming events unlike the silicon antifoam.
Alternative Method to Horner's Approximation for Multirate Analysis of Pressure Buildup
Authors: Useche Marcos , Franco Francy 
Published: November 2019

Abstract
This paper presents a method to calculate an equivalent production rate and time (tp), that models the transient effect of a multirate production period prior to a buildup test. The proposed solution increases the accuracy of Pressure Transient Analysis (PTA).
The traditional Horner method estimates the production time (tp) using the cumulative production and the last production rate; however, this method is an intuitive but not rigorous solution that puts emphasis on the most recent nonzero production rate. There are currently commercial applications that consider the superposition effects of complex production history for PTA analysis, nevertheless, superposition can include a material balance error. The proposed technique is an alternative to superposition that uses the transient effect generated for each production period to estimate the equivalent rate and production time.
A reservoir simulation model with known petrophysical parameters, fluid properties, pressure, and temperature, is used as a reference to evaluate the accuracy of the proposed method. The studied reservoir produces through a well that uses a multirate schedule that includes two different scenarios: (1) a progressive increase in each new production rate and (2) a progressive decrease in each new production rate. Then, the well is shut-in, starting a buildup pressure response. Production time and rate are estimated by applying the Horner's approximation and the proposed solution. Finally, the results from both methods are compared with the known parameters of the reference model.
The proposed method provides a more accurate solution to buildup analysis compared to the Horner time approximation by using the production rate history. The approach can also be applied to drawdown analysis and pressure derivative analysis, keeping the precision of the calculations.
Drill Bit Selection Optimization Based on Rate of Penetration: Application of Artificial Neural Networks and Genetic Algorithms
Authors: Abbas Ahmed K. , Assi Amel H. , Abbas Hayder , Almubarak Haidar , Al Saba Mortadha 
Published: November 2019

Abstract
The drill bit is the most essential tool in drilling operation and optimum bit selection is one of the main challenges in planning and designing new wells. Conventional bit selections are mostly based on the historical performance of similar bits from offset wells. In addition, it is done by different techniques based on offset well logs. However, these methods are time consuming and they are not dependent on actual drilling parameters. The main objective of this study is to optimize bit selection in order to achieve maximum rate of penetration (ROP). In this work, a model that predicts the ROP was developed using artificial neural networks (ANNs) based on 19 input parameters. For the modeling part, a one-dimension mechanical earth model (1D MEM) parameters, drilling fluid properties, and rig- and bit-related parameters, were included as inputs. The optimizing process was then performed to propose the optimum drilling parameters to select the drilling bit that provides the maximum possible ROP. To achieve this, the corresponding mathematical function of the ANNs model was implemented in a procedure using the genetic algorithm (GA) to obtain operating parameters that lead to maximum ROP. The output will propose an optimal bit selection that provides the maximum ROP along with the best drilling parameters. The statistical analysis of the predicted bit types and optimum drilling parameters comparing the actual flied measured values showed a low root mean square error (RMSE), low average absolute percentage error (AAPE), and high correction coefficient (R2). The proposed methodology provides drilling engineers with more choices to determine the best-case scenario for planning and/or drilling future wells. Meanwhile, the newly developed model can be used in optimizing the drilling parameters, maximizing ROP, estimating the drilling time, and eventually reducing the total field development expenses.
Carbonate Lithology Identification with Machine Learning
Authors: Nanjo Takashi , Tanaka Satoru 
Published: November 2019

Abstract
Machine learning has attracted the attention of geoscientists over the years. In particular, image analysis via machine learning has promise for application to exploration and production technologies. Demands have grown for the automation of carbonate lithology identification to shorten the delivery time of work and to enable unspecialized engineers to conduct it. The image analysis of carbonate thin sections is time consuming and requires expert knowledge of carbonate sedimentology. In this study, the authors propose an image analysis technique based on deep neural network for carbonate lithology identification of a thin section, which is an important image analysis process required for oil and gas exploration. In addition, the authors consider that porosity and permeability variations in the same facies are controlled by the grain, cement, pore, and limemud contents. If the contents are accurately measured, the porosity and permeability can be determined more accurately than by using traditional methods such as point counting. The elucidation of the complex relation of porosity and permeability is the objective of automation of carbonate lithology identification. To perform image analysis of the thin section, the authors prepared a data set mainly comprising pictures of the Pleistocene Ryukyu Group, which were composed of reef complex deposits distributed in southern Japan. The data set contains 306 thin section pictures and annotation data labeled by a carbonate sedimentologist. The rock components was divided into four types (grain, cement, pore, and limemud). A convolution neural network (CNN) was utilized to train the model. After training the neural network, each of the four categories was interpreted by the trained model automatically. Resultantly, the accuracy of automatic Dunham classification was 90.6% and the mean average test accuracy of category identification was 83.9%. The interpretation seems highly consistent between human vision and machine vision in both the overview and pixelwise scales. This result indicates that it has sufficient potential to assist geologists and become a basic tool for practical applications. However, the accuracy of category identification is still insufficient. The authors believe that the model requires higher quality supervised data and a greater number of supervised data.
Unconventional Oil - Decline Permeability Multipliers for Model Calibration
Authors: Li James , Fan Li , Zhang Xu 
Published: November 2019

Abstract
The unconventional fracture model (UFM) has been routinely used to model complex fracture systems. The UFM generates both geometry and conductivity of simulated hydraulic fracture networks, which can be used to create unstructured grids for production simulation. The production simulation model generated from the UFM must be calibrated with actual production data so that it can be used for production predictions and different sensitivity analyses such as well spacing, landing point evaluation and completion optimization.
The calibration of the production simulation model is done by specifying oil production rate and history matching the bottomhole pressure (BHP), gas-oil ratio (GOR), and water cut (WCT) measured from oil production wells. The history-matching process mainly involves modifications of the geometry (height and length) and conductivity (permeability) of the hydraulic fracture system, as well as the stimulated reservoir volume (microfractures) surrounding the hydraulic fractures. Modification of the hydraulic fracture geometry usually requires rerunning of the UFM modeling process, which is time consuming. The modification of the hydraulic fracture conductivity usually requires the use of different permeability multipliers in different fracture regions that are defined arbitrarily. To make these modifications, a consistent and systematic process, a permeability multiplier function, has been developed and successfully used in different projects. The function and its application will be introduced and discussed in this paper.
The decline permeability multiplier (DPM) function is defined with three parameters: the permeability multiplier at the initiation point (wellbore) of the fracture, the permeability multiplier at the endpoint (tip) of the fracture, and the curvature of the decline between the two points. By adjusting these three parameters, pressure and production data (BHP, GOR, and WCT) can be reasonably history matched. In practice, the function can be applied to hydraulic fractures and microfractures separately with different parameter values. The function can be used not only to define conductivity distribution inside hydraulic fractures, but also to help initialize water saturation distributions in hydraulic fractures in either structured grids or unstructured grids. An example of water saturation distributed with this decline function to better match the water cut is also presented in the paper.
Bridging Compressor and Expander Technologies in SSLNG Processes
Authors: Drewes Michael , Patel Tushar 
Published: November 2019

Abstract
Today, small-scale liquified natural gas (SSLNG) plants are planned and built in different areas around the globe. Due to the overall market situation and competition, these projects are challenged to decrease capital expenditure (CAPEX), while becoming increasingly efficient to meet mid-size investors' operating expenditure (OPEX) targets and return on investment (ROI) expectations. The main challenges are the overall efficiency of the plant, seal leakage rates, operational flexibility and the plant's space limitations.
To a big extent, the aforementioned points are closely connected to liquefaction technology selection (either single mixed refrigeration or nitrogen Brayton cycle) as well as the rotating equipment used: Firstly, regarding energy use, the refrigeration compressor is the main power consumer in an SSLNG plant (in addition to pumps and smaller compressors). Secondly, a large amount of process leakage is linked to the seals of the rotating equipment. Regarding the third point, operational flexibility, this parameter is closely related to the deployed compressor and expander, and their respective process characteristics. Lastly, the footprint and equipment size have an impact on the installation costs and ultimately CAPEX.
Often, especially in a nitrogen Brayton cycle, compressors as well as warm and cold turboexpanders are supplied as single skid each: that is, a nitrogen compressor skid as well as both warm and cold expander compressors installed on another skid. To reach their future objectives, some SSLNG plant operators are taking new approaches that combine these two technologies: compressor and expander applications are installed on one single gearbox and skid – this is called a Compander. This approach is already used in other industry segments and applications, including LNG carriers. Atlas Copco's first land-based LNG refrigeration Compander was installed back in 2002 at a plant in Norway. The Compander design allows for only one gearbox on which compressor and expander stages are mounted, one oil system, one control system and one seal gas panel – instead of having all of these components twice. By applying these bridging technologies, SSLNG plants are finding new ways to improve OPEX while at the same time reducing the financial burden on new projects. In this case study, we discuss how SSLNG plants in Norway and customers in other places have implemented Atlas Copco Gas and Process integrally geared technology that merges the functions of a centrifugal compressor and turboexpander into one compact Compander unit. In addition, different configurations of separate compressors and expanders are discussed and compared to a single-skid (Compander) solution.
During the discussion, the benefits of a Compander compared to single and separate equipment designs are evaluated.
Innovative ROV Manipulator Powered by Artificial Intelligence to Decrease Risk in Subsea Operations
Authors: Bakr Wael 
Published: November 2019

Abstract
The scope of this paper is to present a new and innovative technology to enhance the performance of subsea installation and construction activities while reducing the risk for both human life and the environment. The conventional method of conducting subsea installation in diver-accessible areas is usually by the use of saturation diving systems and teams. This poses a significant operational risk on the lives of the team, the environment, and the subsea assets in the area, along with also having operational limitations. This paper presents a technology that reduces this risk and enhances subsea installation activities altogether.
The technology presented is an intelligent manipulator that can be installed on Remotely-Operated Vehicles (ROVs). The system relies on artificial intelligence, the geometry of machine making, power, and accuracy to enhance subsea installation operations. Through the use of 3D point cloud technology, the system is able to give accurate views and measurements of the surrounding environment, allowing for accurate path determination and decision making.
The result of the technology is that the ROV manipulator is able to mimic human divers’ mental and physical abilities in different modes of action during subsea installation operations, especially in hazardous environments. This enables such operations to be performed faster and in a more cost-effective manner. Moreover, a key result of the use of this technology is eliminating the huge risk suffered by human divers in hazardous saturation diving environment.
With the use of this technology, the industry would be able to utilize ROVs to conduct diver-less subsea installation activities in a safer, more efficient, and more cost-effective manner than by using saturation diving systems and teams.
Utilizing Machine Learning for a Data Driven Approach to Flow Rate Prediction
Authors: Alkhalaf Ayman , Isichei Obi , Ansari Najmul , Milad Rashad 
Published: November 2019

Abstract
In this study, we aim to demonstrate how machine learning can empower computational models that can predict the flow rate of a given well. Given current real-time data and periodic well tests, this new method computes flow rates using data-driven model. The computational model is based on analyzing the relations and trends in historical data. Relational databases include huge amounts of data that have been accumulated throughout decades. In addition, there is a large number of incoming operational data points every second that gives a lot of insight about the current status, performance, and health of many wells. The project aims to utilize this data to predict the flow rate of a given well.
A variety of well attributes serve as inputs to the computational models that find the current flow rate. Artificial Neural Networks (ANN) were used in order to build these computational models. In addition, a grid search algorithm was used to fine-tune the parameters for the ANN for every single well. Building a single unique model for every well yielded the most accurate results. Wells that are data-rich performed better than wells with insufficient data. To further enhance the accuracy of the models, models are retrained after every incoming patch of real-time data. This retraining calibrates the models to constantly represent the true well performance and predict better. In practice, Flow rate prediction is used by production engineers to analyze the performance of a given well and to accelerate the process of well test verification. One of the main challenges in building unique models for every well is fine-tuning the parameters for the artificial neural networks, which can be a computationally intensive task. Parameter fine-tuning hasn't been discussed in previous literature regarding flow rate prediction. Therefore, our unique approach addresses the individuality of every well and builds models accordingly. This high-level of customization addresses the problem of under-fitting in ANN well models.
First CO2-EOR Project of the Middle East, Lessons Learnt and Future Plan after Two Years of Injection
Authors: Alklih Mohamad Yousef , Al Hammadi Khaled Eissa 
Published: November 2019

Abstract
Experiences gained from CO2 flooding to improve oil recovery worldwide indicates that considerable amount of remaining oil can be recovered with miscible CO2 injection under appropriate conditions. Though its performance in the heterogeneous Middle Eastern carbonate reservoirs has not been well demonstrated yet, it is most likely going to be the future hydrocarbons recovery process in Abu Dhabi after the successful implementation of the first miscible CO2-EOR pilot in 2009 by ADNOC Onshore. In 2016, ADNOC Onshore embarked on field scale implementation of CO2 injection; making it an integral part of the company's overall strategy to utilize CO2 as one alternative option to the expensive HC gas and address related key technical and business aspects.
The currently implemented CO2-EOR has been facing several complexities and challenges including, but not limited to, sustainability of CO2 supply, achieving miscibility conditions, optimization of production and injection, reservoir modeling issues, HSE risks, suitability of monitoring strategy, keeping the CO2 concentration levels within the existing plan design limits, maintaining the integrity of the facilities, associated operating costs and expansion of field development by CO2-EOR.
A number of mitigation plans and actions have been also put in place to ensure the smoothness of operations and therefore maintain the positive impacts of enhanced oil recovery by CO2 injection. Strong management support, multi-disciplinary data integration, continuous surveillance and pro-activeness are considered as the pillars for CO2-EOR success. This work aims to discuss the lessons learned from the ongoing development of CO2-EOR, after 2 years of implementation, and the way forward for the future development phases.
An Automated Kick Alarm System Based on Statistical Analysis of Real-Time Drilling Data
Authors: Mao Youli , Zhang Peng 
Published: November 2019

Abstract
Early influx/kick detection has been one of the most important focus areas for drilling risk management because it enables the crew to take corrective actions that can minimize the danger and cost associated with the event. This work presents an automated influx alarm system that can analyze real-time drilling data using a statistical approach and raise early kick alarms of different levels. Two real-time trend-analysis methods, the divergence of moving average (DMA) and the divergence of moving slope average (DMSA), are applied to quantify trend evolutions of three indicators: normalized rate of penetration (D-exponent), flow rate, and mud pit volume. An overall kick index is then calculated as a weighted summation of the variations in three kick indicators. An alarm is triggered after the calculated kick index surpasses a preset threshold. This automated system is tested against historical drilling data from 15 wells, of which a total of 23 kick events are recorded. The results show that all actual kick events are captured at an average of 8.5 minutes before the recorded time. The ultimate goal of this work is to apply such a highly automated system for early kick detection from real-time drilling data, without requiring additional downhole sensors or expensive flow meters.