bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Approach for System Instability Prediction Using Nodal Analysis
Authors: Fan Yilin , Sarica Cem 
Published: September 2019

Abstract
System instability prediction is essential when designing a production system and/or providing operational adjustment to maintain a stable production. The conventional system Nodal Analysis articulates that the system is unstable to the left of the minimum of the Outflow Performance Relationship (OPR) curve where the well loads up. However, recent data shows that there are stable production points on the left of the minimum of the OPR curve, especially for low permeability shale plays. In this work, a new practical model is presented for both conventional and unconventional wells using Nodal Analysis with a novel approach.
The new approach is based on the derivative analysis of the inflow performance relationship (IPR) and OPR at a nodal point of the bottom hole. Perturbation analysis is used to facilitate the explanation of the new model. It shows that the system is stable when the absolute value of slopes or derivatives of the IPR is greater than that of OPR. To evaluate this concept, transient numerical simulations were conducted using a commercial transient simulator at various IPR conditions, including different permeabilities, for both vertical and horizontal wells. Meanwhile, the concept is also compared with available experimental and field data.
The transient simulation and the available data presented in this study demonstrate that there are stable production operating points on the left of the minimum of the OPR curve. The system stability also depends on the reservoir permeability, i.e., the flow rate corresponding to the onset of instability decreases with decreasing permeability. The new approach predicts this trend well. Overall, the new model matches well with observation from the experiments, field data, and the transient numerical simulations.
Best Practices of Exploration: Integration of Seismic and Electrical Prospecting
Authors: Vorobev Vladimir , Safarov Ildar , Mostovoy Pavel , Shakirzyanov Lenar , Fagereva Veronika 
Published: September 2019

Abstract
Eastern Siberia is characterized by the extremely complex geological structure. The main factors include multiple faults, trappean and salt tectonics, the complex structure of the upper part of the section (0–1200 m) and its high-velocity characteristic (5000–6000 m/s), the high degree of rock transformation by secondary processes, low formation temperatures (10–30°C), the mixed fluid composition (gas, oil and water), and low net thicknesses (5–7 m) of productive layers. The fields of the region are among the most complex ones in the world according to the BP Company's statistics. New seismic and geologic model based on complex analyses of core, well logs, well tests, seismic and electromagnetic data allowed the Gazpromneft-GEO company to drill a series of successful wells.
Gazpromneft-GEO, LLC.holds three oil and gas exploration and production licenses within the Ignyalinsky, Vakunaisky and Tympuchikansky (Chona field) subsurface blocks (Russia, Eastern Siberia, Irkutsk Region and Republic of Sakha (Yakutia)). The area of the blocks is 6,855 sq.km, 3,050 sq.km of which are covered by the 3D seismic and high-density electric prospecting (Fig. 1). 70 exploration wells were drilled. The geological oil reserves of the Chona field are about 500 Mt. Nonetheless, the level of study of the blocks is extremely irregular, despite such significant reserves. To transfer from the exploration stage to production the capital-intensive scope of exploration is required, which expands the exploration program in time and can influence the final economic profitability of the project. The optimization of drilling costs by minimization of drilling wells and the efficiency of their drilling is the key purpose of the project.
The work was carried out within the frames of scientific research and field works at the Gazpromneft-GEO, LLC. fields in Eastern Siberia. The high-density full-azimuth ground-based seismic using the UniQ technology was performed in Russia for the first time. The electric exploration with the near-field time-domain electromagnetic method was carried out along the same lines for the first time in Russia as well. This allowed to form the high-density cube of geoelectric properties. Model based on the wells (Facies model, Petrophysics model) and field geophysical data (3D seismic survey, 3D electric exploration, gravimetric survey, magnetic survey) complexation was made. The use of the approach allows to reduce the number of wells required for exploration of fields by 40%.
Fugitive Emissions Study: How to Minimize Emissions While Utilizing Sand Separators
Authors: Wasfy Rick , Spady Darcy , Hierath Kyle 
Published: September 2019

Abstract
The goal for all oil and gas production facilities is to have zero fugitive emissions. This is impossible but reducing the unnecessary fugitive emissions to zero is possible with planning and correct production equipment selection. Sand separators are a critical piece of early production equipment and every time the sand is removed from these vessels’ fugitive emissions are created. In order to reduce the unnecessary fugitive emissions horizontal or low angle Desanders have shown better performance over vertical or spherical sand separators as they have a larger sand holding capacity resulting in fewer blowdowns and unnecessary fugitive emissions.
For 1 m3 (264.2 gallons) of sand stopped by each Sand Separator the Vertical geometry would produce 72% of the Carbon of the spherical vessel while the horizontal Desander would produce 26% of the spherical and the low angle tilt would produce 11%.
To optimize the process further, a scale system should be paired with the horizontal or low angle Desander so that the Desander is only blown down when needed to further reduce fugitive emissions.
Anisotropic Fault Leakage into Multiple Shallower Formations Connected to an Injection Zone
Authors: Mosaheb Mojtaba , Zeidouni Mehdi 
Published: September 2019

Abstract
Faults can conduct fluids to shallower formations that may adversely affect groundwater resources and ultimately surface environment during geological storage of carbon dioxide. In this study, an analytical model is derived for a multilayer system that is intersected by a leaky fault. The fault-zone structure and anisotropic flow inside the fault zone are considered which are necessary for fault leakage characterization. The fault-zone structure includes damaged zones with high permeability and a core zone in between with low permeability. The diffusivity equations are written for the multilayer system as well as the fault zone and the system of equations is solved by Laplace and Fourier transforms. The solution is capable to investigate up-, across-, and along-fault fluid flow as well as pressure variations of the shallower formations. Using the pressure variations of the injection well, the vertical extension of fault leakage to shallower formations is evaluated.
A New Mechanistic Model for Oil-Water Emulsion Rheology and Boosting Pressure Prediction in Electrical Submersible Pumps ESP
Authors: Zhu Jianjun , Zhu Haiwen , Cao Guangqiang , Banjar Hattan , Peng Jianlin , Zhao Qingqi , Zhang Hong-Quan 
Published: September 2019

Abstract
As the second most widely used artificial lift method in petroleum industry, ESPs help maintain or increase flow rates by converting kinetic energy to hydraulic pressure. During the entire life of an oilfield, water is invariably produced with crude oil. As the field ages, the water cut in production increases. Due to high shear force inside rotating ESPs, the oil-water emulsions may form, which can be stabilized by natural surfactants or fine solids existing in the crude oil. The formation of emulsions during oil production create high viscous mixture, resulting in costly problems and flow assurance issues, such as pressure drop increase and production rate lost. This paper, for the first time, proposes a new mechanistic model for predicting oil-water emulsion rheology and its effect on the boosting pressure in ESPs. The model is validated with experimental measurements with an acceptable accuracy.
The new mechanistic model starts from Euler equations for centrifugal pump, and introduces a conceptual best-match flowrate QBM, at which the outlet flow direction of ESP impeller matches the designed flow direction. The mismatch of velocity directions, resulted from varying liquid flow rates, is used to derive recirculation losses. Other losses due to flow direction change, friction, and leakage flow etc. are also incorporated in the new model. QBM is obtained by matching the predicted performance curve with the catalog curve for water. With the best match flow rate determined, the ESP hydraulic head under viscous fluid flow can be calculated. For oil-water emulsions, a new rheology model based on Brinkman (1952) correlation is developed, which accounts for ESP rotational speed, stage number, and interfacial properties etc. By incorporating the rheology prediction model into mechanistic model, the ESP boosting pressure under oil-water emulsion can be obtained.
The mechanistic model-predicted ESP water performance curves are found to match the catalog curves perfectly. With high-viscosity fluid flow, the model predictions of ESP boosting pressure agree well with the experimental data. For most calculation results within medium to high flow rates, the prediction error is less than 15%. With oil-water two-phase flow, the proposed rheology model predicts the effective viscosities of emulsions match testing results with 10% prediction error. The inversion points, at which the continuous phase changes from oil to water as water cut increases, are also predicted. The predictions of ESP boosting pressure under oil-water emulsion flow by coupling the mechanistic model and emulsion rheology model are comparable with experimental results.
Standardization: Successful Implementation of Driving Consistency in Deepwater Operations
Authors: Durey Daniel A , Murphy Darren J , Rueda Beatriz E 
Published: September 2019

Abstract
Deepwater wells are the most complex and challenging operations for today's petroleum workforce. These challenges push the limits of technology requiring high level personnel competencies and stringent safety requirements. Robust and consistent procedures aid in implementing reliable operational execution. When complex operations include multiple drill ships and TLPs, and when these activities are mirrored by separate support teams of engineers and operations there are opportunities for varying procedures, content, format, and technology applications. This misalignment evolves over time, based on individual preferences, lessons learned, and varying procedures from different service providers.
This paper discusses the efforts and outcomes of bringing standardization to Deepwater operations in the Gulf of Mexico (GOM) and to Shell's broader global Deepwater organization (DWO). Standardization efforts include full End-to-End well delivery from engineering design documents, recommended/best practices, operational procedures, workflow processes, after-action-reviews, knowledge sharing, and refreshing standards as required.
Ensuring a learning loop process is in place and actively used is a key element in keeping standard documents evergreen and has the overarching goal of preventing repeat failures and NPT events. An additional benefit is the ability to deliver documents with structured content, aligned format and standard language to both the operations teams and service providers.
The formation of a core team and central department has driven global standards, active sharing of learnings across all Deepwater business units, opened communication lines with areas previously siloed due to location, reduced cycle time for the engineering teams in re-creating procedures and demonstrated sustainable reductions in operational costs.
Monitoring Dynamic Reservoir Pressure Responses Through Cement
Authors: Garcia Elias , Stutes Gerald , Nåden Christoffer , Borgersen Kjetil 
Published: September 2019

Abstract
During the last five years, the use of permanent downhole gauges has proliferated in the industry. The availability of true bottomhole pressure (BHP) is imperative in validating/improving reservoir models. Similarly to the extrapolation of BHP from surface readings, the use of BHP to extrapolate formation pressure may lead to significant errors in reservoir models that do not provide operators with the competitive edge needed in the current market. Consequently, there is a drive to monitor formation pressure in-situ by placing pressure and temperature gauges in direct contact with the formation.
In recent years, operators have been drilling larger holes, deploying gauge systems on the exterior of the casing, and cementing the gauge systems in place for multiple purposes. In artificial lift applications, cemented gauge systems have helped operators to avoid costs of decompleting and redeploying gauge systems on tubing whenever the electric submersible pumps (ESP) must be serviced, or perhaps whenever operators want to convert an observation well to a producing well.
In unconventional plays, technologies involving quartz pressure and temperature gauges, oriented perforating, and well conditioning practices can enable operators to deploy multiple real-time downhole pressure and temperature gauges on casing across long horizontal sections of a wellbore. This, in turn, can provide valuable production data with which to understand cluster production performance, cross-well communication, fracture azimuth, well spacing, and stage-length production implications.
Cemented gauges enable operators to understand pressure dynamics in the overburden, cap rock, or reservoir sections. The permanently installed, casing-deployed gauges connect to the surface through cable or through deployment of wireless inductive coupling technology.
Unleashing the Power of Smart Particles for Completion Diagnostics: Advancing the Production Flow Profiling Technology with Sub-Atomic Fingerprints and Big Data Analytics
Authors: Shokanov Talgat , Khudorozhkov Pavel , Shokanov Adilkhan K. 
Published: September 2019

Abstract
Current multistage hydraulic fracturing operations in shale are costly, environmentally challenging and inefficient. Multistage hydraulic fracturing operations already represent close to 60% of the total drilling and completion cost for each shale well. The industry studies reported that based on data evaluated in multiple shale basins in North America alone that up to 50% of the clusters and stages do not produce in geometric completion design. Shale E&P operators need more accurate, cost-efficient, timely and actionable data on the performance of individual fracturing stages and intra-well communication to enable improved decision-making and optimization of multistage hydraulic fracturing and completion strategy, as well as overall field development.
This paper will describe a revolutionary smart tracer portfolio testing and design for multistage hydraulic fracturing stimulation. The technology enables the next generation of smart tracers coupled with advanced sub-atomic measurements that significantly reduce the completion cost and double the efficiency of the hydraulic fracturing treatments. An automated process with stringent quality control assured precise tracer addition onsite and provided accurate and actionable completion diagnostics results at fraction of the cost for high-cost measurements (e.g., PLT, DTS & DAS).
The integration of smart tracer portfolio with intelligent-completion diagnostics for E&P customer enabled by performance-flow-profile data. This data used to optimize completion strategies, achieve optimal production per foot, and reduce completion cost. Follow-up big-data analytics and 3D fracture-modeling delivered accurate, calibrated, actionable, and cost-effective completion-diagnostics results. Since tracer data are captured over several months, E&P operators are captured access to continuous flow profiling data to optimize well performance routinely when new completion-diagnostics results are received. This will enable E&P operators to significantly reduce operating cost and optimize production in shale wells.
Machine Learning for Well Log Normalization
Authors: Akkurt Ridvan , Miller Mike , Hodenfield Brooke , Pirie Iain , Farnan David , Koley Manas 
Published: September 2019

Abstract
A well log measurement can be modelled as the sum of three components: the formation signal, random noise, and systematic error. The sources for systematic error include tool malfunctions, shop and field miscalibrations, operator error, and inherent hardware design limitations. Log calibration, more commonly referred to as log normalization, is the process of applying corrective shifts to well logs to minimize the systematic error.In this paper we develop a machine learning approach to the multi-well log normalization problem which we believe is particularly applicable in unconventional field studies involving hundreds of wells of varying data quality and vintage.
We start by applying machine learning to the multi-well normalization problem, where the reference unit and reference wells are selected by the geoscientist. The reference unit is typically a laterally extensive stratigraphic interval with a consistent log response over the area of interest, which in our case is a tight limestone with small amounts of dolomite and/or silt. The reference wells are those that do not require any normalization. A predictive machine learning model is trained using log data from the reference unit in the reference wells, and a regression-based optimization algorithm is used to solve for constant shifts which are applied as normalization corrections for density and neutron logs in the remaining wells.
The process of selecting reference wells and picking the boundaries of the calibration unit can be subjective; it is influenced by the experience of the geoscientist with the area of interest, the pressures of project timelines and the availability of sufficient resources. The impact of these human-introduced biases can be severe in large projects where a team of geoscientists is engaged to process hundreds of wells: inconsistent normalization practices can lead to large errors in computed reservoir properties.
As a solution to minimize such inconsistencies, we propose to extend the use of machine learning to create a seamless workflow that is automated and requires minimal user involvement in the execution phase. Towards this objective, we incorporate an additional machine learning component, which eliminates the requirement for a-priori knowledge of the reference unit boundaries. The resulting workflow produces consistent and high-quality results that compare very well to those produced by manual Log Normalization workflows run by experts.
Comparison of machine learning based results against expert answers show that the machine learning approach offers an efficient alternative to manual log normalization where gains can be significant in projects involving large numbers of wells, as in the case of unconventional plays.
Drainage Areas, Shapes, and Reserves for Wells in Reservoirs with Multiple Fluid Contacts / Constant Pressure Boundaries
Authors: Prasun Samir , Kumar Anil 
Published: September 2019

Abstract
Well drainage areas and shapes for wells in reservoirs with aquifer contact or gas-oil contact (constant pressure boundary) take varying shapes, such as, water coming from one-side (edge water drive) to water coming from 3-sides when the 4th-side is a sealing fault. This information is important in well test interpretations, peripheral flooding, aquifer injection, gas and CO2 aquifer storage, geothermal reservoirs, and any subsurface recovery schemes involving injection-production well pairs. A knowledge of well drainage shapes is helpful in optimizing well placement, well productivity estimations, and to maximize reserves.
Results show that for a 2-well 3-sided water-drive reservoir system, each well drains an equal area when the well-rates are equal; however, the drainage areas of a well increases logarithmically with an increasing ratio of its flow rate to that of an adjacent well. In case of a multiple well system, results show that the drainage area of well closer to water contact is smaller than that of an interior well, farther away from the water-contact. Results are presented in graphical form and equations to determine the drainage area and shapes for varying production rate-ratios between wells in a multiple constant pressure boundary rectangular reservoir. An algebraic procedure is presented to generalize results from a 2-well system for extension to a multi-well system with similar multiple constant pressure boundary conditions. The equations yield result within 5% of those obtained from complex streamline simulations. A well's location within its drainage area is needed to determine mean well pressures from MBH (Matthews-Brons-Hazebroek) functions for water drive reservoirs, and to estimate productivity index and cumulative water influx volumes.
Fast-Track Qualitative Interpretation of Seismic Data in a Permanent Reservoir Monitoring PRM Setting for a Brazilian Field
Authors: Maleki Masoud , Danaei Shahram , Davolio Alessandra , José Schiozer Denis 
Published: September 2019

Abstract
Permanent Reservoir Monitoring (PRM) in systems deep-water settings provide on-demand snapshots for hydrocarbon reservoirs at different times during their production history. Delays in the interpretation turn-around of 4D seismic data reduce some benefits of the PRM. These delays could adversely impact the decision making processes despite obtaining information on demand. Using fast-track approaches in 4D seismic interpretation can provide timely information for reservoir management. This work focuses on a fast-track 4D seismic qualitative interpretation in PRM environment, with the aim of choosing the best seismic amplitude attribute (4D) to use. Different seismic attributes are extracted and the one with high signal-to-noise ratio is selected to carry out the 4D qualitative interpretation. All 4D signals are juxtaposed with well production history data to increase confidence in our interpretation. The selected attribute can be interpreted and used for the foreseeable life of field. This workflow has been developed and applied on post-salt Brazilian offshore field to choose the best seismic attribute to conduct the 4D seismic qualitative interpretation.