bluuebunny/crossref_metadata_2025_split huggingface dataset:
Detecting Failures and Optimizing Performance in Artificial Lift Using Machine Learning Models
Authors: Pennel Mike , Hsiung Jeffrey , Putcha V. B. 
Published: April 2018

Abstract
This paper will review the different types of artificial lift problems that have been diagnosed and predicted using machine learning algorithms including tubing and pump failure along with suboptimal performance of sucker rod pumps and and gas injection. The discussion will review the suboptimal performance periods that can be diagnosed and the associated impact on well performance. The presentation will focus on artificial lift and specifically on sucker rod pumps and gas lift.
The machine learning models were trained using production data for over 1 year from operating wells in the Bakken. Time series sensor and controller data for 800 wells was loaded into an AWS cloud platform for analysis and profiling. Experts in each lift type reviewed failure and performance data and identified periods of failure modes and sub-optimal performance. These events were used to train different types of machine learning and artificial intelligence algorithms include random forest, gradient boosted tree and neural network models. These models were used to diagnose and predict the problem states. The overall performance of the models was analyzed to rank and assess performance of each model, lift type and problem state.
Data science, machine learning and artificial intelligence are revolutionizing industries by applying massive low-cost computing power to optimize machine performance. Artificial lift is a good use case given the increasing number of sensors and signals being produced by the latest controllers, the improving data connectivity, lower cost of compute and data storage and the complexity of the surface and downhole problems in unconventional wells. The tubing and pump failure models proved to be highly accurate with diagnostic model accuracy &gt; 99% and precision ranging from 50% to 60%. The long-term tubing failure model performance achieved an expected estimation error of just 31 days on a range of asset lifespans from 100 to 600 days. Suboptimal performance models had accuracy &gt; 90%. The model performance enables high confidence interaction with the wells to react more quickly to problem states and thereby reduce downtime and improve pump effectiveness.
One of the key challenges in deploying data science in the field is to facilitate cooperation between engineers, operators and data scientists. Most of the advanced models used in other industries are based on data from on-line systems (e.g. Google, Facebook) with consistent data and defined outcomes. The oil field has unique data and environmental challenges. This paper discusses how experts in the artificial lift interacted with data scientists to understand this dynamic operating environment, the challenges with data quality and how these assets operate in the field.
Engaging and Educating our Community: A Case History of the California Regulatory Environment and a Local O&G Company's Path on Building an Ambassador Program to Empower its Employees
Authors: Lal T. 
Published: April 2018

Abstract
This paper focuses on California's regulatory environment and our role as industry, individuals, and the general public, in shaping its future. It provides a historical context of regulations, the current focus areas, and challenges moving forward. Improving our understanding of the energy industry empowers us to collaborate in shaping the future of California. The sustainability of the industry is important to the sustainability of the state - from families to small businesses to large employers.
Since the Industrial Revolution, energy has been the backbone of human society. Medicine, agriculture, science, technology and more are all sustained by energy. Energy demand is categorized broadly as electricity and transportation demand. Oil is the major energy source for transportation, due to availability, reliability, and economics. In the U.S., roughly 50% of the oil consumed is imported (EIA 2017). In California, the number is closer to 67% (Californians for Energy Independence 2015). One reason for high oil imports is the challenging regulatory environment.
Traditionally, the Oil and Gas (O&G) industry has relied on classic lobbying-based negotiations with agencies. However, with increasingly, widespread misunderstanding of the industry and an ever-changing landscape, it is crucial to incorporate a "boots on the ground" approach. Stakeholder engagement is key in responding to and exposing misleading tactics and messages of some industry critics.
Aera Energy LLC (Aera) has been a leader in implementing an Ambassador Program, an education program focused on providing employees an opportunity to share simple, straightforward, and accurate messages with their families and local community. With safety and environmental responsibility as the foundation for all activities, it is the "Aera Way" of doing business. We hope you are interested to learn more and see how you can be an industry Ambassador within your local community!
Integrated Stratigraphic Controls for Flow Simulation of the Wall Creek Member in the Frontier Formation: Western Powder River Basin, Wyoming
Authors: Le T. T. , Hoffman T. , LaFontaine N. , Hofmann M. 
Published: April 2018

Abstract
The Frontier Formation in the Powder River Basin has been re-discovered for oil and gas potential with the development of long horizontal wells and multi-stage hydraulic fracturing. Over the last decade, the Wall Creek member (WCM) of the Frontier formation has proven to be a successful hydrocarbon-producing target, yet a full understanding of the flow behavior of this complex stratigraphic unit has not been fully achieved.
The fluid and rock properties have uncertainty and are not well defined due to the low permeability rocks. This study aims to describe the fluid flow behaviors of these features and create an outcrop model that includes all the reservoir properties and geologic features to better understand hydrocarbon recovery. This project consists of two distinct aspects: (1) defining the reservoir properties through a well flow model and (2) upscaling the permeability of the reservoir models with different geologic features into an outcrop model for the WCM.
A single horizontal well flow simulation model was created to estimate the reservoir properties. Using three offset well logs, a 32 feet interval was selected to represent the net pay zone of the Wall Creek. The porosity was estimated using well logs, and permeability was established by applying a correlation of porosity and permeability found from core data. The historical production data was matched by modifying the initial fluid saturations and the rock physics parameters such as relative permeability and capillary pressure. As a result, representative fluid and rock physics models were obtained for the outcrop model.
From the outcrop study, defined geologic models with different facies of Wall Creek member were created to include abundances and orientations of mud drapes as the most impacted features that may affect the the fluid flow ability. An outcrop model captures fine heterogeneities of all the facies using flow-based upscaling of the geologic models. The effective directional permeabilities of each facies were obtained to integrate into an outcrop model to capture the geologic features that may have a large impact on the hydrocarbon recovery.
In this work, we developed methods to incorporate fine-scale (cm) geologic observations from the outcrop with well scale properties from the field in an integrated study that was ultimately used to help determine field level decisions such as well spacing and fracture spacing.
Data-Driven Production Forecasting of Unconventional Wells with Apache Spark
Authors: Mohammadmoradi Peyman , Moradi Hessam M. , Kantzas Apostolos 
Published: April 2018

Abstract
Real-time decision making, field surveillance, and production optimization improve the performance of existing operations to increase hydrocarbon recovery and reduce emissions. In this regard, the oil and condensate flow metering in offshore gas condensate platforms is always confronted by environmental, economic, and operational challenges resulting in uncertain production management plans. Although production forecasting of unconventional gas condensate systems is more challenging than for conventional wells, it is of great interest to support decisions by knowing the future of the wells as far as possible. The virtual flow metering techniques make it possible to utilize daily production data sets and extract information on how wells and reservoir will respond to different operational conditions. The objective of this study is to embed artificial intelligence algorithms in reservoir uncertainty modeling and present a mechanistically-supported data-driven model applicable for production forecasting of gas condensate wells with higher confidence. The outcome entails a new set of mathematical models, implemented using Apache Spark cluster computing engine with APIs in Python, that enables rigorous and robust optimization of the recovery process, designing and discovering hidden patterns in production data, and extracting reservoir information indirectly in seconds. The observations used to demonstrate the performance of the proposed hybrid model include 1600 well-testing data points together with 420 days of production history of an offshore gas condensate platform. The daily platform production is allocated efficiently to individual wells using a multilayer perceptron neural network model adaptively trained with well-testing and daily production datasets, and supported by the Energy and mass balance equations.
Continuous Variable Pressure Steam Injection for Enhanced Oil Recovery
Authors: Li Y. , Agarwal A. , Kovscek A. R. 
Published: April 2018

Abstract
Continuous variable pressure steam injection is a new approach to manage heat flow within a reservoir and reduce the temperature of fluids arriving at producers thereby avoiding shut-in caused by high temperature. An optimal injection scenario with varied pressure and constant steam quality is shown to have the largest oil recovery given the same amount of energy injected under various reservoir settings. The economic effects were evaluated to select the optimal variable pressure injection schedule with the greatest Net Present Value (NPV).
Various scenarios with different injection pressures and steam qualities were investigated and compared based upon aspects of oil recovery, energy consumption, and periods of producer shut in. Sensitivity analysis was performed regarding permeability, well distances, heat losses, as well as geomechanical model parameters including Young's Modulus, Poisson's Ratio, and initial stress. Grid refinement experiments dictated the optimal unit grid dimension for accurate 3D modeling including geological heterogeneity. Reservoir subsidence was also investigated. The variable-pressure injection cycles were designed and evaluated using an economics model.
Results teach that the proposed steam injection schedules provide continuous oil production (i.e., no producer shuts in) with the largest energy efficiency and economic benefits. The hybrid injection strategy for a 3-D heterogenous case showed an increase in 15% more oil recovered compared to the constant pressure injection scenario after 10 years of production. We conclude that steam injection design effectively controls the reservoir temperature and avoids producer shut-in. The sensitivity analysis further supports the feasibility of this optimal scenario under various reservoir conditions. Inclusion of geomechanics provides important information about reservoir deformation, but geomechanics has little effect on the oil produced or the optimal injection strategy. Importantly, the subsidence results do not indicate well failure under the conditions studied. By performing a systematic investigation, the combination of continuous high and low constant-pressure steam injection for fixed time intervals yields the largest NPV.
Experimental Determination of the Phase Transition Point in Gas Condensates using a Cost-Effective Semiautomated Isochoric Apparatus
Authors: Salahshoor Shadi , Fahes Mashhad 
Published: April 2018

Abstract
A robust high precision experimental approach to determine dew point pressure of gas condensates in the laboratory is proposed in this study. Gas condensate reservoirs have been the center of attention for numerous numerical and experimental studies for decades. Their perplexing fluid flow and phase behavior results in various production challenges including condensate banking and compositional changes due to retrograde condensation accompanying production from these reservoirs. Therefore, accurate prediction of dew point pressure (DPP) is crucial in developing long-term production plans for these reservoirs.
Isochoric method, an indirect high precision way of DPP and phase transition condition determination, is commonly used in other disciplines where a clear non-visual determination of phase transition of a fixed volume of fluid is needed. This study provides an insight into this approach in determining DPP for a binary mixture of hydrocarbons. A semi-automated apparatus for measuring and monitoring equilibrium conditions along with fluid properties is designed based on the isochoric method. The apparatus provides constant volume, variable pressure (0 to 1500 psi), and variable temperature (290 to 410 K) experimental conditions. Pressure and temperature measurements are used to detect the phase transition point along the constant mole-constant volume line based on the change in the slope of this line at the transition point.
Results are plotted on the phase envelope (P-T diagram) of the same mixture using different equations of state and the accuracy of each of these equations of state in providing the most reliable prediction of DPP is analyzed. Reproducibility of the data is examined and error estimation for the entire experiment is provided. This experimental method is inexpensive, less time consuming, and more accurate compared to other PVT experiments and is applicable for multicomponent systems. It does not require gas expulsion or sample recombination throughout the procedure and could be identified as the only reliable way of quantifying the effect of porous media on phase behavior.
Simulation and Modeling of Pressure Difference on Steam Chamber and Production Performance
Authors: Xiong H. , Huang S. , Devegowda D. , Liu H. , Kim C. 
Published: April 2018

Abstract
Steam Assisted Gravity Drainage (SAGD) is the most effective thermal recovery method to exploit oil sand. The driving force of gravity is generally acknowledged as the most significant driving mechanism in SAGD process. However, an increasing number of field cases have proven that pressure difference may even play an important role in some cases. Therefore, the objectives of this paper are to simulate the effects of injector-producer pressure difference on steam chamber evolution and SAGD production performance.
A series of 2D numerical simulations are conducted on the basis of MacKay River and Dover reservoir in West Canada to investigate the influence of pressure difference. Meanwhile, the effects of pressure difference on oil production rate, stable production time, steam chamber development were studied in detail. Moreover, by combining Darcy's law and heat conduction along with a mass balance in the reservoir, a modified mathematical model considering the effects of pressure difference is established to predict the SAGD production performance. Finally, the proposed model is validated by comparing calculated cumulative oil production and oil production rate with the simulation results.
The results indicate that the oil production first increases rapidly and then slows down when a certain pressure difference is reached. The impacts of pressure difference are much greater at steam chamber rising stage than at steam chamber expansion stage. Therefore, in the field, at the beginning of the SAGD recovery, it is better to augment pressure difference, while at the steam expansion stage, a lower pressure difference is preferred, so that a smaller chance of steam breakthrough and a higher economic benefit will be achieved. Besides, it is found that steam chamber expansion angle is a function of pressure difference. Based on this phenomenon, a new mathematical model is established considering the modification of the expansion angle which Butler treated it as a constant. With the proposed model, production performance, such as cumulative oil production and oil production rate can be predicted. Steam chamber shape is redefined at the rising stage and it changes from fan-shape to hexagonal-shape, but not the single fan-shape defined by Butler. This shape-redefinition can clearly explain why the greatest oil production rate does not happen when steam chamber reaches the cap-rock.
Literature surveys show few studies on how pressure difference influences steam chamber development and SAGD recovery. This paper provides a modified SAGD production model and also a totally new scope for SAGD EOR, which makes the pressure difference a new optimizable factor in field.
Integrating Designed Simulations for Bayesian Identification of Geological Controls on the Performance of a Multilayer Heterogeneous Sandstone Oil Reservoir: Applied on the CO2-Assisted Gravity Drainage Process
Authors: Al-Mudhafar Watheq J. , Rao Dandina N. 
Published: April 2018

Abstract
Determining the most influential parameters affecting the reservoir flow responses is a vital step in the integrated reservoir studies for evaluation and analyses. More specifically, removing the non-influencing parameters leads to reach the optimal process design. The conventional procedure to determine the most sensitive parameters combines regression analysis with analysis of variance. However, that approach produces one reduced regression model after eliminating the non-influential parameters (deterministic approach). In this paper, Bayesian Model Averaging (BMA) was applied to stochastically identify the geological parameters that control the immiscible CO2-assisted gravity drainage process performance in a multilayer heterogeneous sandstone oil reservoir in South Rumaila oil field. After achieving acceptable history matching within approximately 57 year of production, the CO2-assisted gravity drainage injection was evaluated in 10 year future prediction. In that process, vertical wells were placed at the top of the reservoir for CO2 injection to formulate a gas cap that make oil drains down towards the bottom of reservoir. Above the oil-water contact, a series of horizontal production wells were installed to produce oil. The main geological parameters that controls the immiscible CO2 flooding are horizontal permeability, anisotropy ratio (Kv/Kh), and porosity that were investigated for their impact by layers or multilayer. Many low-discrepancy designed simulations were created by the Latin Hypercube Design and evaluated by the reservoir flow simulation to disregard the no-impact parameters using the linear BMA approach. Among multi- candidate models, BMA select the best model of optimal subset variables based on the highest posterior probability. BMA led to quantify accurate impact of each geological control on the CO2 EOR process. Based on the concept of the CO2-assisted gravity drainage process, it was concluded that horizontal permeability has a higher impact of the reservoir flow response than vertical permeability (Kv/Kh) with no effect of porosity.
Data Ethics in Oil and Gas Operations
Authors: Ershaghi Iraj , Ershaghi Milad A. , Popa Andrei 
Published: April 2018

Abstract
The use of data-driven cognitive solutions represents a major advancement in the management of oil and gas operations. Tools that integrate concepts from disciplines such as informatics, machine learning and predictive analytics can offer powerful solutions to allow improvements in the safety, efficiency and integrity of oil and gas operations. We discuss data systems that enhance traditional, human-based monitoring systems, with the aim of approaching risk-free operations.
Ethical decision-making is critical in the management of oil and gas operations. Digital data solutions effectively compensate for the limitations of human-based decision processes when confronted with data overload and multi-dimensional data systems. Increased adoption of data gathering, automation, data analytics and advanced computer-aided process control has already made its mark in the industry. Examples have emerged as for how in areas such as artificial lift, pipeline transportation and offshore operations, these data analytics techniques have helped in failure detection and prediction and smart management of such operations. The incorporation of data-intensive decision-making and smart risk management solutions have resulted in a step change in the improvement of the ethical foundation and the base underlying the industry. Moreover, these digital tools and machine-based cognitive processes for risk-avoidance solutions can help to build and restore the public's faith and trust in the industry. We also discuss how relying on digital solutions alone can have its limitations when it comes to professional ethics and responsibilities.
Updated Classification of Lost Circulation Treatments and Materials with an Integrated Analysis and their Applications
Authors: Alkinani Husam H. , Al-Hameedi Abo Taleb , Flori Ralph E. , Dunn-Norman Shari , Hilgedick Steven A. , Alsaba Mortadha T. 
Published: April 2018

Abstract
Lost circulation materials (LCM's) have been widely used to avoid or stop losses. Due to a large number of currently available materials, treatments, and their different applications, classification of them is very important. The most recent materials and treatments classification was published a long time ago. This paper intends to present an updated classification including conventional and new technologies. Here, LCM's are re-classified based on their appearance, applications, and chemical and physical properties.
Lost circulation control during well construction is more than just selecting the right lost circulation material (LCM), but requires a complete engineered approach. This paper was developed mostly based on the literature with real field data from various Basra's oil fields. More than 200 technical journals papers, textbooks, case histories, real field data, and manuals that address the problem of lost circulation are carefully reviewed and summarized. Lost circulation materials and treatments are extracted from the literature. The results from the lab are compared to what was applied to many oil fields including 800 wells drilled in Basra's oil fields.
A new classification for lost circulation treatments and materials is developed. Unlike the current classification, the new classification is based on the applications. Conventional LCM's are currently classified into different categories based on their appearance as fibrous, flaky, and granular or a blend of all three.
This paper classifies the lost circulation materials and treatments as corrective and preventive approaches. The corrective treatments can be applied after the occurrence of the lost circulation; however, the preventive techniques can be applied before entering losses zones as a proactive action. The most recent developments in lost circulation materials and treatments have been discussed, in addition to the presentation of a comprehensive summary of today's available LCM's, treatments and alternative approaches with their applications.
This paper discusses the most recent developments in materials and treatments with their applications. Treatments and materials are tabulated for each type of losses to provide effective remedies and minimize NPT and cost. The purpose of this study is to develop practical guidelines that are general and not biased towards a particular service company product which will also serve as a quick reference guide for lost circulation prevention and control at the well-site for drilling personnel.