bluuebunny/crossref_metadata_2025 huggingface dataset:
Active Student Engagement in Learning - Using Virtual Reality Technology to Develop Professional Skills for Petroleum Engineering Education
Authors: Retnanto Albertus , Fadlelmula Mohamed , Alyafei Nayef , Sheharyar Ali
Published: September 2019

Virtual reality (VR) technology, which is the most immersive type of reality technology, can be used in simulating real workplace for occupational safety, trainings, and educational purposes. It encourages students to develop deeper understanding of content. In addition, it allows active learning that transitions students from being mere recipients of information to being participants actively engaged in the learning process. The implementation of this technology makes it possible to improve the effectiveness of the education by simplifying what is normally challenging to understand in physical classrooms. For example, VR can be used to help in visualizing and understanding the differences between different offshore oil and gas rigs. Field trips are one way of adding variety to instruction, thus optimizing teaching effctiveness while motivating student learning. Field trips help bridge formal and informal learning by connecting the students to the real-world experiences. Despite research confirming enhanced learning, sometimes it is not feasible to arrange the fild trips due to several logistical and safety reasons. This study set out to bring real-world petroleum facilities to the classroom with the help of VR technology in the Middle East campus. The goal is develop a tool that overcome the restrictions that are associated with the real field trips, increase the students’ interest and enhance their learning experience by using innovative teaching method in petroleum engineering courses. The developed VR-based field trips allowed petroleum engineering students to experience a range of field areas at a fraction of the cost of conventional real field trips, whilst forgoing restrictions due harsh climate and limited accessibility in relative comfort, addressing gender and disability equality within engineering education. Besides all this, the VR-based field trips allowed the students to access the reservoir, which is not possible in reality. Finally, the student feedback on the VR-based field trip demonstrate positive support of learning methods as it creates amazing experiences, and they feel it is exciting and engaging. In addition it also provided an effective way of emphasizing the learning process in petroleum engineering education and promotes active learning for new generations.
Data-Analytics-Based Wireline Formation Tester Adaption and Optimization Study, West China Ultradeep Formation
Authors: Guo Qingbin , Qiu Bin , Zhao Yuanliang , Fan Zhaoya , Chen Jichao , Han Yifu , Zhang Tao , Li Kaixuan , Yu Hua , Jiang Lei , Wei Guo , Yu Daiguo
Published: September 2019

The Kuqa foreland thrust belt, as a secondary tectonic unit of the Tarim basin at the front of the Tianshan Mountains, is a foreland basin that formed in the Late Tertiary. The lower Cretaceous Bashijiqike tight sandstone in the basin is an ultralow-permeability and low-porosity reservoir. The Kuqa foreland thrust belt includes Kela, Keshen, Bozi, Zhongqiu, and Alvart blocks. Although these blocks developed under the same sedimentary conditions, the permeability-porosity relationship and wireline log response can be very different among the blocks. Whereas the shallow zone has been had E&P activities for decades, fully understanding the fluid properties, the porosity-permeability relationship, and distribution pattern of gas in the deep to ultradeep zone is of strategic significance and can provide the experience for the exploration of similar gas reservoirs in China and worldwide. The main target zone depth varies from 6000 m to 8000 m, and the formation pressure is near or exceeds 20,000 psi. Compared to a time-consuming and costly drillstem test (DST) operation, the wireline formation test (WFT) is the most efficient and cost-saving method to confirm hydrocarbon presence. However, the success rate of WFT sampling operations in the deep Kuqa formation is less than 50% overall, mostly due to the formation tightness exceeding the capability of the tools. Therefore, development of an optimized WFT suitable to the formation was critical.

More than 30 WFT wells in Kuqa foreland thrust belt were studied to understand the well and formation conditions causing the success or failure of these WFT operations. By doing a statistical analysis of more than 1000 pressure test points, we researched the relationship between mobility and petrophysical logs such as neutron, density, gamma ray, resistivity, P-sonic, etc. Several statistical mathematic methods were applied during this study, including univariate linear regression (ULR), multiple linear regression (MLR), neural network regression analysis (NNA), and decision tree analysis (DTA) methods. A systematic workflow was formed to mine data information, and we delivered a standard chart of the relationship between mobility and the petrophysical logs, an integrated equation based on MLR, and an NNA model that can be applied to WFT feasibility analysis.

These methods can be considered the foundation of artificial intelligence (AI), which can be used in future mobility automatic prediction. This provides a rough estimation of the mobility and sampling success rate and enables WFT optimization to be conducted in advance.
Innovative Artificial Intelligence Approach in Vaca Muerta Shale Oil Wells for Real Time Optimization
Authors: Romero Quishpe Adriana , Silva Alonso Katherine , Alvarez Claramunt Juan Ignacio , Barros Jose Luis , Bizzotto Pablo , Ferrigno Eugenio , Martinez Gustavo
Published: September 2019

A well is in natural flowing state when its bottom-hole pressure is enough to produce to the surface. Natural flowing well’s production is regulated by using surface restrictions to regulate the production rate in such a way that the overall well performance is a function of several variables. Examples of these variables are tubing size, choke size, wellhead pressure, flow line size, and perforation density. This implies that changing any of these variables will modify well performance. One of the techniques for the analysis of production performsnce is studying the wellhead pressure declination, since, in critical flow conditions, flow is a function of wellhead pressure. From wellhead pressure trends you can identify the behavior of each well and determine some issues, such as: choke erosion due to sand production, choke o tubing paraffin plugging or choke obstruction. In order to achieve an effective real-time monitoring of this type of wells, and in this way reduce the production losses, the challenge was to create online tools that could detect those mentioned issues.

The present work performs the analysis of wellhead pressure curves using data science, with the purpose of predicting real time anomalies that could occur for timely correction. The data correspond to 130 flowing wells from the Loma Campana Field. The study began with a filtering process of the pressure curve, with two specific objectives: first, eliminate atypical values from the time series, and second, smooth the curve in such a way that future predictions can be performed. Next, the Prophet methodology was applied with the purpose of predicting values of the curve. This is based on historicsl values of the time series to predict future values; the trend characteristic of the curve was used to apply this methodology. Then, to identify the anomaly a model was designed based on the declination of the curve. The pressure declination curve is a descending exponential function, so the first and second derivatives indicate the trend (ascending - descending) and curvature (concave or convex) of it. Once these values are available, they are classified according to the anomaly: paraffin, encrustation or obstruction. Finally, the model is being tested in the Loma Campana control room, delivering a probability of occurrence of any anomalies every hour.
Experimental Study of Gas-Liquid Diffusion in Porous Rocks and Bulk Fluids to Investigate the Effect of Rock Matrix Hindrance
Authors: Lou Xuanqing , Chakraborty Nirjhor , Karpyn Zuleima , Ayala Luis , Nagarajan Narayana , Wijaya Zein
Published: September 2019

The design of oil recovery processes by gas injection or vapor solvent relies on knowledge of diffusion coefficients to enable meaningful production predictions. However, lab measurements of diffusion coefficients are often performed on bulk fluids, without accountability for the hindrance caused by the pore network structure and tortuosity of porous media. As such, our ability to predict effective diffusion coefficients in porous rocks is inadequate and, additional laboratory work is needed to investigate the impact of the medium itself on transport by diffusion. In addition, experimental data on multi-phase diffusion coefficients are particularly scarce for tight rocks. This study therefore proposes an experimental methodology, based on a pressure-decay technique, to measure diffusion of injected gas in oil saturated porous rocks. A diffusion experiment of gas into bulk oil (without porous medium) provides an upper limit estimation of this gas-liquid diffusion coefficient. Diffusion experiments using limestone and Bakken shale provide insight into different degrees of restriction in high permeability versus low permeability media. Two analytical models and one numerical model were implemented and compared to determine the diffusion coefficients from the time-dependent experimental pressure-decay data. These diffusion coefficients were found in agreement with literature on corresponding data, demonstrating the validity of the modeling approaches used. Results indicate considerable hindrance to diffusion in porous media relative to bulk oil and relates to the tortuosity and constrictivity of the rock matrix. The diffusion coefficient of methane in bulk oil is 3.8 × 10−9 m2/s. In our limestone sample, this diffusion coefficient drops by one order of magnitude, ranging between 1.5 to 6.5 × 10−10 m2/s and, it drops by another order of magnitude in the Bakken shale sample to 2.0 × 10−11 m2/s.
Development of the Subsurface Process and Reinjection Compressor
Authors: Brady Jerry , Passmore Kevin , Paskvan Frank , Wilkes Jason , Allison Tim , Swanson Erik , Klein John
Published: September 2019

This paper discusses component developments, validation testing, and yard testing of the subsurface process and reinjection compressor (SPARC) prototype tool approaching downhole flowing conditions (≈1200 psig and > 225°F). This is the first time a compressor and turbo expander have been built small enough to be run through tubing and operated autonomously from the surface.

A brief review of the overall system design and critical component design and testing are followed by a detailed review of the surface testing of the entire prototype machine at simulated downhole conditions. The SPARC concept uses the excess production pressure (energy) that is usually wasted across a choke or elsewhere in the production system to generate power through a downhole turbo-expander that runs a downhole gas compressor to reinject a portion of the gas stream. The system consists of a downhole separator, compressor, turbo-expander and other standard downhole equipment for the necessary plumbing.

The successful test results of the bearing and thrust disk component testing at up to 1,000 psig and > 450°F are provided, followed by the successful yard test results of the entire SPARC prototype machine at downhole flowing conditions, including all the rotating equipment (turbo expander, compressor, and shaft), in situ process-lubrication system, and autonomous controls.

This equipment will allow for the reduction of costly surface facilities to process, compress, and reinject produced gas into North Slope fields and some oil and condensate fields elsewhere globally, which are limited in liquid hydrocarbon production because of surface gas processing facility limitations.

Another potential use of the SPARC technology is as an artificial lift mechanism for gas reservoirs. Using the SPARC as a gas well artificial lift system would require a redesign of the SPARC with an electric motor as its power source in place of the turbo-expander. However, no new technology breakthroughs are necessary because the technology has already been developed with the SPARC design.

To date, there have been no small gas compressors, turbo expanders, and other necessary equipment built and tested that can be run through 4 1/2-in. tubing/casing and operate autonomously at downhole conditions. This technology opens up the possibilities of additional relatively inexpensive gas recycling on the North Slope and other condensate fields worldwide.
Impact of Asymmetric Stimulated Rock Volume on Casing Deformation in Multi-Stage Fracturing; A Case Study
Authors: Yu Hao , Dahi Taleghani Arash , Lian Zhanghua , Lin Tiejun
Published: September 2019

Microseismic data and production logs in our study area have confirmed an asymmetric development of the stimulation rock volume, while severe casing deformation problems have been reported frequently in this area. In this paper, we investigate the possibility of casing failure due to strong shear stresses developed by asymmetric stimulated zones. Overlapping stimulation zones in adjacent stages may intensify asymmetry of the pore pressure distribution and resultant shear forces. Although induced shearing may have a positive impact on fracture permeability, but it may also cause operational problems by inducing severe casing deformations. While most of the casing deformation models only consider rock deformations very close to the wellbore, we developed a 3D coupled model for fracture network growth and stress re-distribution during hydraulic fracturing to achieve a more realistic model for casing deformation. This reservoir-scale model is tied to a more detailed near-wellbore model including the casing and cement sheath to simulate casing deformations. Case studies were conducted using data from a shale gas well that experienced severe casing deformation during hydraulic fracturing. Impact of stage spacing, and pumping rate are incorporated to investigate their potential impacts on casing and well integrity. Multi-stage hydraulic fracturing considering the development of complex fracture network is simulated at the reservoir scale based on the microseismic events. Continuous re-distribution and re-orientation of stress field near the borehole are tracked during the development of the fracture network which reveals some pocket of tensile stresses along the casing. Asymmetric fractures are observed to generate strong shear stress on the suspended casing. These shear forces result in deflection and S-shape deformations. Some regions receive repeating treatments, which leads to increase formation stress heterogeneity and worsen casing deformation severity. Our analysis has indicated that simply increasing the flexural strength by increasing thickness of casing cannot radically mitigate casing deformation problems. This paper provides a novel workflow for a coupled modelling of casing deformation during hydraulic fracturing operations, while current modelling efforts assume symmetric fracture geometries.
Case Study: Optimizing Eagle Ford Field Development Through a Fully Integrated Workflow
Authors: Morales Adrian , Holman Robert , Nugent Drew , Wang Jingjing , Reece Zach , Madubuike Chinomso , Flores Santiago , Berndt Tyson , Nowaczewski Vincent , Cook Stephanie , Trumbo Amanda , Keng Rachel , Vallejo Julieta , Richard Rex
Published: September 2019

An integrated project can take many forms depending on available data. As simple as a horizontally isotropic model with estimated hydraulic fracture geometries used for simple approximations, to a large scale seismic to simulation workflow. Presented is a large-scale workflow designed to take into consideration a vast source of data.

In this study, the team investigates a development area in the Eagle Ford rich in data acquisition. We develop a robust workflow, taking into account field data acquisition (seismic, 4D seismic and chemical tracers), laboratory (geomechanical, geochemistry and PVT) measurements and correlations, petrophysical measurements (characterization, facies, electrical borehole image), real time field surveillance (microseismic, MTI, fracture hit prevention and mitigation program through pressure monitoring) and finally integrating all the components of a complex large scale project into a common simulation platform (seismic, geomodelling, hydraulic fracturing and reservoir simulation) which is used to run sensitivities.

The workflow developed and applied for this project can be scaled for projects of any size depending on the data available. After integrating data from various disciplines, the following primary drivers and reservoir understanding can be concluded. At a given oil price, optimum well spacing for a given completion strategy can be developed to maximize rate of return of the project. Many operators function in isolated teams with a genuine effort for collaboration, however genuine effort is not enough for a successful integrated modelling project, a dedicated multidisciplinary team is required.

We present what is to our knowledge, one of the most complete data sets used for an integrated modelling project to be presented to the public. The specific lessons from the project are applied to future Eagle Ford projects, while the overall workflow developed can be tailored and applied to any future field developments.
Permanent Storage of CO2 in Mexican Igneous Rocks
Authors: Cantú-Apodaca Erick , Ortiz-Lucas Pilar , Gómora-Figueroa Ana Paulina , Mancera-Alejández Javier , González-Torres Enrique
Published: September 2019

Mexico has undertaken a series of measures to implement Carbon Capture and Storage (CCS) technology due to the acquired national and international commitments regarding the reduction of GHG emissions as well as their implications to climate change. So far, the screening of the different methods for geological storage of CO2 potentially applicable in Mexico includes deep saline aquifers, non-economic coal beds, and crude oil reservoirs for EOR.

Storage of carbon dioxide projects in igneous rocks has been successful in the past. According to the literature, there are about eight sites, onshore, in the world with plenty of igneous rocks to store CO2, and Mexico is one of these sites. However, little attention has been paid to the study of igneous rocks as a potential method for geological storage of carbon dioxide.

This work aims the first petrographic and geochemical analysis to estimate the feasibility of CO2 mineralization thorough Mexican basaltic formations. This analysis includes the identification of the main CO2 emission areas in Mexico, the collection of samples near to large CO2 emissions sites and, the characterization of the samples by different tests and techniques. The properties of the rock samples and water were studied after the tests to compare changes in mineral phases and water composition.

The results herein confirm the feasibility of Mexican basalts to react in the presence of CO2 but also set the recommendations for the next steps to take to determine the technical feasibility of the CO2 permanent storage using basalts in Mexico.
Exploiting Shale Creep Deformation to Create Annular Barriers for Well Plugging and Abandonment: Experimental Investigation and Numerical Simulation
Authors: Enayatpour Saeid , Thombare Akshay , Aldin Munir , van Oort Eric
Published: September 2019

Oil and gas wells produce hydrocarbons for a limited number of years, and at the end of their production life they need to be plugged and abandoned. This process has to be done in a safe and economic way. Creep deformation of shale rock in uncemented casing sections may simplify well abandonments considerably. Creep can close the annular gap between a shale formation and an uncemented section of a casing string, generating a barrier that prevents hydrocarbons from flowing to the surface on the annular side. Wells with such a "shale-as-a-barrier" generated by creep now only require abandonment plugs on the inside of the casing, without the need for installation of additional annular barriers. This may eliminate such operations as casing milling and casing pulling, thereby allowing e.g. offshore abandonments to be done rigless, at significantly reduced cost.

This paper presents the first results of an experimental investigation and numerical modeling study into the nature of the "shale-as-a-barrier" phenomenon. Specifically, we focus on laboratory and field scale numerical simulation of creep behavior of North Sea Lark shale rock for oil and gas well plug and abandonment purposes. In our Finite Element simulations of the shale creep phenomenon, we have used the time-hardening creep model, which assumes a non-linear relationship between creep strain and stress, temperature and time. The model parameters were obtained from a curve fit of laboratory experimental results conducted for a creeping shale. Then, using the experimentally-derived parameters, numerical simulation was performed for a laboratory scale model and result was validated against laboratory results. Once this validation had taken place, the model size was extended to the field scale for prediction of annular closure time and barrier formation. Simulations show a strong correlation between rock stiffness and annular gap closure time, as expected; hence, the success of any "shale-as-a-barrier" project is a distinct function of shale rock stiffness. Lowering near-wellbore stiffness artificially may accelerate annular barrier creation of slowly creeping shale formations.
Methods for Late Life Solvent Recovery Enhancement in Thermal-Solvent Based in-Situ Recovery Processes.
Authors: Khaledi Rahman , Farshidi Forough , Motahhari Hamed Reza
Published: September 2019

Non-thermal-solvent and thermal-solvent based heavy oil recovery processes are technologies in which solvent is used as either the main or the secondary agent, in conjunction with heating, for bitumen viscosity reduction. In these processes a hydrocarbon solvent is injected into the reservoir and produced back with the recovered bitumen. A fraction of the injected solvent is retained in the reservoir at an equilibrium state as gas and liquid phases. Since the cost of injected solvent in these processes is a major portion of the operating cost, recovery of the retained solvent from the reservoir at the end of bitumen depletion stage results in recovery of significant capital and thus improvement of the process economics.

Imperial-ExxonMobil have been optimizing the existing and developing new recovery technologies to improve the efficiencies, economics and environmental performance of heavy oil production operations. Recent focus has been on developing solvent based recovery processes through an integrated research program that includes fundamental laboratory work, advanced numerical simulation studies, laboratory scaled physical modeling, and field piloting. The research program aims at in-depth investigation and understanding of process physics and mechanisms to allow evaluating and optimizing process performance.

In this paper, development of a new method for recovery of the retained solvent from the reservoir at the end of the bitumen depletion stage is introduced. This method takes advantages of solvent vapor-liquid thermodynamic equilibrium to strip the retained solvent from the reservoir. A stripping gas is injected and circulated in the bitumen depleted chamber to vaporize and recover the retained solvent to the surface. The reservoir modeling results show that this method is very effective and efficient in accelerating recovery of the retained solvent. The physical modeling experimental data confirms the effectiveness of this method. Field pilot data from a solvent assisted recovery process are presented which demonstrate solvent recovery efficiency using continuous steam injection.
A Utica Case Study: The Impact of Permeability Estimates on History Matching, Fracture Length, and Well Spacing
Authors: Fowler Garrett , McClure Mark , Cipolla Craig
Published: September 2019

Maximizing economic performance in shale requires optimal selection of well and cluster spacing, among other parameters. Reservoir engineering calculations can be used to optimize spacing, but these calculations are impacted by uncertainties in input parameters. System permeability is particularly important and difficult to measure. Diagnostic Fracture Injection Tests (DFIT's) are often used to estimate permeability because they provide a direct, in-situ measurement. However, in recent work, it was shown that conventional DFIT interpretation techniques can overestimate permeability in gas shale by two orders of magnitude. In this study, the impact of the permeability estimate is demonstrated using a dataset from the Utica/Point Pleasant. Production data is history matched with models assuming high and low permeability. It is possible to history match both models because of non-uniqueness between fracture area and permeability. Sensitivity analysis simulations are performed to assess the impact of well and cluster spacing on net present value. Relative to the high permeability model, the low permeability model has a greater optimal well spacing and a tighter optimal cluster spacing. The comparison shows that improved accuracy in the permeability estimate significantly improves economic performance. The low permeability model has much earlier production interference than the high permeability model because the low permeability model requires greater effective fracture length to match production. This is consistent with the operator's experience that outer wells outproduce inner wells within weeks or months from the start of production.
Use of Visualization Labs to Enhance Petroleum Engineering Education
Authors: Menouar Habib , Heinze Lloyd , Watson Marshall , Gamadi Talal
Published: September 2019

The quest for excellence and efficiency in academia and education is critical. Some of the best proven methods of assessing educational programs refer to the standards and approach developed by ABET (The Accreditation Board for Engineering and Technology). This institution which was initiated originally in the US is involved in the supervision and accreditation of more than 3750 educational programs spread internationally over 30 countries.

The Bob L. Herd Department of Petroleum Engineering has been using these standards since its ABET accreditation 66 years ago. It is still striving for excellence, improvement and perfection since then. The goal is to produce the best petroleum engineers using the last Student's Outcome approach (SO) and assessment methods as developed by ABET.

ABET approach uses various tools to assess educational programs, some of which are conventional such as: Examinations, quizzes, guided homework's, term projects etc. others are more involved. The data are translated into quantifiable parameters for analysis. In addition to all these tools, this study uses one more that has been proven to be valuable and added to the arsenal available at the Bob L. Herd DPE. It is the use of visualization labs. This consists of models built to simulate various forces, physical mechanisms and engineering concepts encountered in drilling, production and reservoir engineering. The present study is directed toward the use of petrophysical or scaled models to enhance the methods of education in petroleum engineering and more specifically in reservoir engineering. Scaled models are not new in the oil industry. They used to be the only way to study oil reservoirs and forecast their behavior until the advent of computers and numerical simulation in the 1970's.

Several scaled models designed to simulate water-flooding processes for educational purpose are used by students to complete projects on reservoir development. Analysis of the results shows the following: After completion of the projects, students have a better understanding of the fundamental concepts and principles behind reservoir engineering, depletion mechanisms and other issues related to fluid displacement in porous media.The Metrix associated with the student outcome and assessment methods developed for ABET evaluation showed that a well-conceived visualization lab can be extremely effective in petroleum engineering education in general and more particularly in reservoir engineering.
Reservoir Characterization-Geostatistical Modeling of the Paleocene Zelten Carbonate Reservoir. Case study: Meghil Field, Sirte Basin, Libya
Authors: Masaud Mohamed , Meddaugh W. Scott
Published: September 2019

Geostatistical-based models provide a considerable improvement for predictive reliability of dynamic models and the following reservoir management decisions. This study focuses on geostatistical modeling the Paleocene Zelten Carbonate reservoir in the Meghil field. The field was discovered in 1959 and production operations began in 1961. Nineteen wells have been drilled to date. The structural framework consists of three slightly asymmetrical anticlinal structures trending NW-SE with steeper dip on the SW flanks. Each of the structures are separated by major normal faults. Seismic interpretation suggests that carbonate build-ups are most likely present on the three separate structures. Edge detection was used to clarify the structural geometries and the presence of additional minor faults. Pillar gridding technique was used to develop the structural framework including four major faults that are partially sealed based on analysis of the available DST and production test data. Stratigraphic analysis indicates a local presentation of dolomitic limestone in the northern portion of the main and the western structures caused considerable litho-facies variation that impacted the distribution of the petrophysical properties. Basic and advanced formation evaluation the net reservoir thickness of about 15 feet with an average porosity of 17% and average water saturation of 35%. Geostatistical-based applications that combine the spatial statistics (e.g. the semivariogram) and the available well and core data were used to populate the reservoir model with porosity, permeability, facies (lithology), net/gross, and water saturation. A conceptual facies model was also used to constrain the reservoir property distributions. Sequential Gaussian Simulation (SGS) was used to populate the model with porosity and water saturation and Sequential Indicator Simulation (SIS) was used to populate the facies model with permeability. The modeling parameters (e.g. semivariogram, correlation coefficients) were significantly constrained by the limited number of wells. Based on the limited number of wells available the semivariogram analysis resulted in a spherical semivariogram model with major axis range of 1435 meters for porosity and 1800 meters for water saturation. Minor axis ranges were about 50% of the major axis ranges. Given the limited well data, a significant effort was made to document the potential impact of the semivariogram parameters on the original hydrocarbon in place (OHIP) estimates and the lateral stratigraphic continuity of reservoir properties. The deterministic approach resulted in place volume estimates of 60 MMBBL and the stochastic approach provided an estimate of 45 MMBBL.
Determination of Total Organic Carbon for Organic Rich Shale Reservoirs by Means of Cores and Logs
Authors: Xiong Hao , Wu Xiaye , Fu Jing
Published: September 2019

Oil production from shale and tight formations will increase to more than 6 million barrels per day (b/d) in the coming decade, making up most of total U.S. oil production (> 50%). However, achieving an accurate formation evaluation of shale faces many complex challenges. One of the complexities is the accurate estimation of shale properties from well logs, which is initially designed for conventional reservoirs. When we use the well logs to obtain shale properties, they often cause some deviations. Therefore, in this work, we combine cores and well logs together to provide a more accurate guideline for estimation of total organic carbon, which is primarily of interest to petroleum geochemists and geologists.

Our work is based on Archie's equation. Resistivity log will lead to some incorrect results, such as total resistivity, when we follow the conventional interpretation procedure in well logs. Porosity is another complex parameter, which cannot be determined only by well log, i.e. density, NMR, and Neutron log. Therefore, the flowchart of TOC calculation includes five main parts: (I) the shale content calculation using Gamma log; (II) the determination of shale distributions using Density and Neutron logs and cross-plot; (III) the calculation of total resistivity at different distribution types; (IV) obtaining porosity using core analysis, NMR and density logs; and (V) the calculation of TOC from modified Archie's equation.

The results indicate that the shale content has a strong effect on estimation of water saturation and hydrocarbon saturation. Especially, the effect of shale content is exacerbated at a low water saturation. A more accurate flowchart for TOC calculation is established. Based on Archie's equation, we modify total resistivity and porosity by combining Gamma Log, Density Log, Neutron Log, NMR Log, and Cross-plot. An easier way to estimate porosity is provided. We combine the matrix density and kerogen density together and obtain them from core analysis. Poupon's et al. (1954) laminar model has some limitations when applying in shale reservoirs, especially at a low porosity.

Literature surveys show few studies on the flowchart of TOC calculation in shale reservoirs. This paper provides some insights into challenges of well logs, core analysis in shale reservoirs and a more accurate guideline of TOC calculation in shale reservoirs.
Integration of Time-Lapse Seismic Data Using the Onset Time Approach: The Impact of Seismic Survey Frequency
Authors: Liu Tian , Hetz Gill , Chen Hongquan , Datta-gupta Akhil
Published: September 2019

The seismic inversion method using the seismic onset times has shown great promise for integrating real- continuous seismic surveys for updating geologic models. However, due to the high cost of seismic surveys, such frequent seismic surveys are not commonly available. In this study, we focus on analyzing the impact of seismic survey frequency on the onset time approach, aiming to extend the advantages of onset time approach when infrequent seismic surveys are available.

To analyze the impact of seismic survey frequency on the onset time approach, first, we conduct a sensitivity analysis based on the frequent seismic survey data (over 175 surveys) of steam injection in a heavy oil reservoir (Peace River Unit) in Canada. The calculated onset time maps based on seismic survey data sampled at various time intervals from the frequent data sets are compared to examine the need and effectiveness of interpolation between surveys. Additionally, we compare the onset time inversion with the traditional seismic amplitude inversion and quantitatively investigate the nonlinearity and robustness for these two inversion methods.

The sensitivity analysis shows that using interpolation between seismic surveys to calculate the onset time an adequate onset time map can be extracted from the infrequent seismic surveys. This holds good as long as there are no changes in the underlying physical mechanisms during the interpolation period. A 2D waterflooding case demonstrates the necessity of interpolation for large time span between the seismic surveys and obtaining more accurate model update and efficient data misfit reduction during the inversion. The SPE Brugge benchmark case shows that the onset time inversion method obtains comparable permeability update as the traditional seismic amplitude inversion method while being much more efficient. This results from the significant data reduction achieved by integrating a single onset time map rather than multiple sets of amplitude maps. The onset time approach also achieves superior convergence performance resulting from its quasi-linear properties. It is found that the nonlinearity of the onset time method can be smaller than that of the amplitude inversion method by several orders of magnitude.
Prediction of Multilateral Inflow Control Valve Flow Performance Using Machine Learning
Authors: Aljubran Mohammad Jabs , Horne Roland
Published: September 2019

Intelligent multilateral well completions provide downhole flow rate, pressure, and temperature measurements at multiple well segments which allows for a continuous spatiotemporal data stream. Such an extensive data input poses a challenging task to decide on the optimal strategy of manipulating the inflow control valve (ICV) settings over time for best performance. This study investigated the use of machine learning to analyze and predict well performance given different ICV settings to ultimately maximize the well output.

A commercial reservoir simulator was used to generate two synthetic reservoir models: homogeneous (Case A) and heterogenous (Case B). These synthetic data were used to train, validate, and test machine learning models. The reservoir cases were generated based on a segmented, trilateral producer completed with three ICV devices installed at tie-in segments. The data used were measurements of wellhead and downhole flow rates across ICV segments over a period of 4,000 days. A total of 1,330 experiments were conducted with an eight-day timestep, generating a total of 667,660 sample data points for each of Case A and Case B. Fully connected neural networks were used to fit the data while model generalizability was enhanced using regularization techniques, namely L2 regularization and early stopping.

Both random sampling and Latin Hypercube Sampling (LHS) methods were evaluated in constructing the training, validation, and testing splits. Trained with different sample sizes drawn from the 1,330 simulated data histories for the two reservoir models, the proposed neural network showed excellent results. Given only ten simulated choices of ICV settings for training, the network proved capable of predicting oil and water production profiles at surface for both homogeneous and heterogeneous reservoir models with over 0.95 coefficient of determination (R2) when evaluated at unseen, test ICV settings. Extending the problem to downhole flow performance prediction, about 40 training simulated settings were necessary to achieve 0.95 R2. We observed that LHS was superior to random sampling in both R2 average and confidence interval. We also found that increasing the training and validation sample sizes increased the test R2 when testing against unseen cases. Study results suggest the applicability of machine reinforcement learning to estimate the well output at different ICV settings, where the neural network model depends fully on the real-time well feedback and production measurements.

By using a machine learning approach during the operation of a well with multiple ICV settings, it would be feasible to estimate the lateral-by-lateral output at unseen scenarios. Hence, it becomes possible to maximize the well output by using an optimization algorithm to determine the optimal ICV settings.
A New Gas Lift-Beam Pump Hybrid Completion Design in Thailand Onshore Field
Authors: Sompopsart Suwin , Toempromraj Wararit , Nadoon Apiwat , Hnuruang Kittithuch , Beokhaimook Chayapod
Published: September 2019

In the onshore field in the Northern part of Thailand, the wells are typically produced with gas lift and converted to beam pump later, using the annulus space for gas separation. In the past, the completion string must be replaced to switch to beam pumps. However, with the new Hybrid completion, the existing completion can be used, and the amount of workover is reduced. In the new Hybrid completion, two sliding sleeves are installed in the tubing string, allowing us to utilize both artificial lift methods without replacing the tubing. To produce the well with gas lift, both sleeves are closed, and the well is produced normally. When converting the well to be produced with a beam pump, both sliding sleeves are opened, a plug is set above the lower sleeve, and a downhole pump installed above the upper sleeve. This forces the wellbore fluid to flow out to the annulus through the lower sleeve. Since the liquid level is higher than the upper sleeve, most of the gas travels up the annulus while the liquid traverses through the upper sleeve from the annulus into the tubing. The liquid is then pumped along the string with a beam pump. This method acts as a gas separation mechanism to prevent gas lock and reduce efficiency problems for beam pumps. The flexibility to switch between the two artificial lift methods allows us to handle the dynamic wellbore and reservoir conditions more efficiently. The Hybrid completion has enabled us to (1) handle a wider well productivity range, (2) significantly lower the cost of workover, (3) decrease the hazards exposure during operations, and (4) produce oil and gas faster, favoring the economic return.
Deep Learning for Well Data History Analysis
Authors: Li Yuanjun , Sun Ruixiao , Horne Roland
Published: September 2019

The rapid development of machine learning algorithms and the massive accumulation of well data from continuous monitoring has enabled new applications in the oil and gas industries. Data gathered from well sensors are a foundation of the oilfield digitization and data-driven analysis. Here, we describe a deep learning approach to predict the long-term well performance based on a moderate duration of well monitoring data.

In this study, we first developed the data processing procedures for oilfield time series data and determined the proper selection of data sampling frequency, parameter combinations and data structures for deep learning models. Then we explored how Deep Learning (DL) models can be employed for well data analysis and how can we combine physics and DL models. Recurrent Neural Network (RNN) is a type of sequential DL model, which can be utilized for time series data analysis. This approach preserves preceding information and yields current response with memory of prior well behavior. Two candidate RNN models were tried to determine how well they were able to improve the accuracy and stability of well performance estimates. These two methods are Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM). In addition, a novel combination of RNN with Convolutional Neural Networks (CNNs), Long- and Short-term Time-series network (LSTNet), was also investigated.

These various models were tested and compared based on the public production datasets from Volve Field. Both GRU and LSTM achieved higher accuracy in performance prediction compared to the simple RNN. In the case of frequent well shut-in and opening, the failure in capturing fast pressure responses and the extreme fluctuations with the simple RNN ultimately leads to high error. In contrast, LSTNet is more stable to frequent or significant well variations. With advanced deep learning structures, engineers can interpret long-term reservoir performance information from responses estimated by deep learning models, instead of performing costly well tests or shut-ins.
Viscoelastic Characterization Effect of High-Viscosity Friction Reducers and Proppant Transport Performance in High-TDS Environment
Authors: Ba Geri Mohammed , Ellafi Abdulaziz , Flori Ralph , Noles Jerry , Kim Sangjoon
Published: September 2019

Viscoelastic property of high-viscosity friction reducers (HVFRs) was developed as an alternative fracturing fluid system because of advantages such as the ability to transport particles, higher fracture conductivity, and potential lower cost due to fewer chemicals and equipment on location. However, concerns remain about using HVFRs to transport proppant in DI water and harsh brine solution (e.g. 2wt% KCl and 10 lbs. brine). The primary objective of this study is to investigate the viscoelastic property that can help to understand the true proppant transporting capacity of fracturing fluids in high-TDS environment.

To address the evaluation performance of HVFRs, a comprehensive review of numerous papers associated to viscoelastic property of hydraulic fracturing fluids were investigated and summarized. This paper also provides a full comparison study of viscosity and elastic modulus between HVFRs and among fracturing fluids such as xanthan, polyacrylamide-based emulsion polymer, and guar. Moreover, viscosity profiles and elastic modulus were conducted at different temperatures. Better proppant transportation effect though higher viscosity through Stoke's law and the effect on proppant transportation from elastic modulus comparison were also investigated. Finally, HVFR Conductivity test and successful field test result were explained.

The results of the experimental work show that viscoelastic property HVFRs provides good behavior to transport proppant. Viscosity profile decreased slightly as the temperature increased from 75 to 150 when the DI water was used. While using 10 lbs. Brine the viscosity was reduced by 33%. The longer polymer chains of HVFR indicated better elastic modulus in DI water. The elastic modulus also indicated that the highest values at frequency 4.5 Hz from each amplitude, and lower values as amplitude was increased. Although high molecular weight HVFRs were utilized on the conductivity test, the results observed that the regained permeability was up to 110%. Finally, the promising results from the case study showed that using HVFRs could be performed economically and efficiently for the purpose of proppant transportation and pressure reduction in high TDS fluids.
Fracturing Perspectives in Deep HPHT Well: Comprehensive Analysis using High-Frequency Pressure Monitoring, Flowback-Rebound and Step-Rate Tests
Authors: Abdrazakov Dmitriy , Parkhonyuk Sergey , Korkin Roman , Nikolayev Max , Dyussembayev Damir
Published: September 2019

Analysis of production perspectives for very deep wells (below 20,000 ft true vertical depth) is closely related to understanding of the hydraulic fracturing perspectives because at these depths, the overburden stresses and long geological history of the target formations usually result in low permeability. Costs of hydraulic fracturing operations in this type of well is significantly higher than in shallower conditions; however, the risk of nonprofitable outcome is significant.

A workflow, including several key methods, is suggested to mitigate the operational and financial risks of potential hydraulic fracturing operations in very deep environments. These techniques employ cost-effective yet technically strong tools for determination of required reservoir parameters and well information, including fracture closure pressure, reservoir pressure, formation transmissibility, and well tubulars integrity. Suggested methods avoid well intervention and associated rig and services costs. The workflow was applied in a very deep and hot well with target intervals below 21,000 ft and helped in estimating the fracturing perspectives in an efficient manner.