bluuebunny/crossref_metadata_2025 huggingface dataset:
An Efficient Hydraulic Fracture Geometry Calibration Workflow Using Microseismic Data, Geomechanics, DFN Models, and History Matching
Authors: Leines-Artieda Joseph Alexander , Liu Chuxi , Yang Hongzhi , Wu Jianfa , Chang Cheng , Yu Wei , Sepehrnoori Kamy
Published: September 2021

Reliable estimates of hydraulic fracture geometry help reduce the uncertainty associated with estimated ultimate recovery (EUR) forecasts and optimize field developing planning in unconventional reservoirs. For these reasons, operators gather information from different sources with the objective to calibrate their hydraulic fracture models. Microseismic data is commonly acquired by operators to estimate hydraulic fracture geometry and to optimize well completion designs. However, relying solely on estimates derived from microseismic information may lead to inaccurate estimates of hydraulic fracture geometry.

The objective of this study is to efficiently calibrate hydraulic fracture geometry by using microseismic data, physics-based fracture propagation models, and the embedded discrete fracture model (EDFM). We first obtain preliminary estimates of fracture geometry based on microseismic events’ spatial location and density with respect to the perforation cluster location. We then tune key completion parameters using an in-house fracture propagation model to provide hydraulic fracture geometries that are constrained by the microseismic cloud. In the history matching process, we included the effect of natural fractures, using the microseismic events location as natural fracture initiation points. Finally, we used cutoff coefficients to further reduce hydraulic fracture geometries to match production data.

The results of this work showed a fast and flexible method to estimate fracture half-lengths and fracture heights, resulting in a direct indicator of the completion design. Additionally, hydraulic-natural fracture interactions were assessed. We concluded that the inclusion of cutoff coefficients as history matching parameters allows to derive realistic hydraulic and natural fracture models calibrated with microseismic and production data in unconventional reservoirs.
Welcome to the 21st Century for Project Managers
Authors: Thompson Analise , Chaban Fady , Strathman Tony , Gönczi David
Published: September 2021

If the O&G industry adopted new mail technology at the same rate it adopts project management technologies, it would still be using the Pony Express. Risk aversion and resistance to change are two of the main reasons for project failure across the industry. The industry still solves problems by throwing a bunch of human resources at the issue. The more people in the room the better the solution will be, right? In the 21st century, project management needs be based on the interaction of technology with human behavior. The objective of this paper is to introduce the industry to project management in the 21st century.

In today's ever-changing global economy, the definition of success is just as fluid, and project management must be agile enough to deal with this. Finding something that works and then sticking to it for decades will no longer suffice. Modern technology companies take a unique approach to major project management which continually polls for changes and empowers individual employees to use their own best judgement while maintaining coordination with their fellows. An examination of this approach can provide helpful insight into optimizing the use of available resources, human or otherwise.

Today's top technologies make it easy for individual team members to continuously update and record the progression of the project, and helps employees work toward better solutions rather than limiting themselves to the original requirements and company protocol. Employees are empowered to look for solutions, think out of the box and outside of what is currently available in-house. In the 21st century, the solution to problems is not a complex spreadsheet shared on SharePoint, it's an elegant integration of technology that optimizes human performance as shown in this case study.
Thermo-Gas-Chemical Stimulation as a Revolutionary Ior-Eor Method by the in-Situ Generation of Hot Nitrogen and Acid
Authors: Rezaei Koochi Mojtaba , Mehrabi-Kalajahi Seyedsaeed , Varfolomeev Mikhail Alekseevich
Published: September 2021

As known, fracture's capacity and penetration are two key factors for fulfillment of the fracturing jobs including conventional and acid fracturing process. Penetration of acid into existing fractures can improve fracture capacity by etching of fracture surface. Increasing temperature of reservoir rock results in reduction of breakdown pressure. Thermo-gas-chemical technology by in-situ releasing of extra hot gases (N2 and steam) and acid provides a series of network with long fractures and permanent conductivity. A series of experiments in high-pressure and high-temperature (HPHT) reactor were designed to understand the performance and effectiveness of thermo-gas-chemical reaction and determine the optimum binary composition in order to release maximum temperature, pressure and acid generation to provide long conductive fractures. In parallel, dependence of breakdown pressure and temperature was modeled. Moreover, to understand the geometry and propagation of fractures, the effect of thermo-gas-chemical method was studied on core samples in core holder and then cores were scanned by 4D tomography. The preliminary results showed that during the thermo-gas-chemical reaction temperature in reaction zone reaches 207 ℃ and pressure 893 psi due to reaction products. It was found that reaction initiates just after the injection of activator and temperature and pressure increased instantly. This phenomenon acts as a strong impact to break formation rock. The pH of aqueous solutions during the reaction decreased from 8 to 1 and below which provides etching the surface of existing and new fractures. Observed that thermobaric parameters of reaction closely depend on the concentration and reaction activator. Experimental results show that the application of thermo-gas-chemical fluid instead of ordinary fracturing fluid, results in reduction of breakdown pressure from 3400 psi to 121 psi, due to induced thermobaric shock. Experiments on core samples and 4D tomography confirmed the formation of new fractures and expansion of existing ones. Thermo-gas-chemical technology by generation of in-situ hot gases and acid can provide a new high efficiency, cost-effective and eco-friendly method of EOR method for tight low permeability reservoirs and even depleted reservoirs without increasing water cut.
Integration of Chemofacies and Rock Mechanical Properties Using Machine Learning Algorithms: Implications for Geomechanics and Hydraulic Fracture Stimulations in Paleozoic Formations, Saudi Arabia
Authors: Hussain Maaruf , Amao Abduljamiu , Al-Ramadan Khalid , Olatunji Sunday , Negara Ardiansyah
Published: September 2021

The knowledge of rock mechanical properties is critical to reducing drilling risk and maximizing well and reservoir productivity. Rock chemical composition, their spatial distribution, and porosity significantly influenced these properties. However, low porosity characterized unconventional reservoirs as such, geochemical properties considerably control their mechanical behavior. In this study, we used chemostratigraphy as a correlation tool to separate strata in highly homogenous formations where other traditional stratigraphic methods failed. In addition, we integrated the chemofacies output and reduced Young's modulus to outline predictable associations between facies and mechanical properties. Thus, providing better understanding of lithofacies-controlled changes in rock strength that are useful inputs for geomechanical models and completions stimulations.
Reassessment of Petroleum Engineering Education: Is It the End of an Era or a New Start?
Authors: Babadagli Tayfun
Published: September 2021

Since emerging more than a century ago, petroleum engineering (PE) education has increasingly kept its popularity despite significant downturns in the industry. During these downturn periods, observed at least four times since the 1973 oil crisis, structural changes in university programs have been considered. On the other hand, during the "heyday" periods, institutions have had to tackle enormous demand from industry, severely increased enrollments, and reestablish resources to provide a proper service. In light of these observations and while experiencing the fifth downturn period over the last five decades, it is time again to ask the same question: "Shall we continue with the same PE education model or radically shift to a new model?"

In this paper, after reviewing more than fifty articles published over the last 85 years reporting the attempts made towards reshaping PE education, an option of restructuring PE programs is discussed. This option is less oil industry (and oil prices) dependent and more of a "general" engineering education program with an emphasis on the "geoscience" or "subsurface" engineering aspects of the PE discipline. Detailed discussions focus on curriculum updates to address the industry practice of "subsurface" related engineering applications. Viability of this option was discussed from industry, academia, and students’ perspective.

This restructuring option requires substantial changes to curricula, skill development, and teaching and learning styles. Fundamentals are essential to include in PE education similar to other general (or major) engineering disciplines such as mechanical, civil, chemical, and electrical engineering. The essential elements of engineering skills such as creative design, decision making, problem description and solving, management under high degree of uncertainty, and data collection and processing for optimization are to be included in the new model. Finally, the model proposed is critically discussed and analyzed from different perspectives (industry, academia, and students) considering current and prospected subsurface engineering applications.
Pressure Transient Analysis of Wells in the Fault-Karst Carbonate Reservoirs with Vertical Beads-on-String Structure: Case Studies in Shunbei Oilfield, Tarim Basin of Northwestern China
Authors: Wei Cao , Cheng Shiqing , Wang Yang , Shang Ruyuan , Zhu Lang , Yu Haiyang
Published: September 2021

The newly discovered the fault-karst carbonate reservoirs in Tarim Basin are formed by large-scale tectonic fault activities and multiple-stage karstification. The ground outcrop, seismic reflection and well logging show that the large caves, vugs, tectonic fractures and matrix coexist in the reservoirs. The fractures interconnecting with caves in series to form vertical beads-on-string structure is the most common pattern. It is found that conventional models are difficult to match the recorded pressure data.

To fill this gap, this work summarizes three kinds of patterns for this structure and presents three novel models to estimate formation properties. The physical models of multi-fracture-region multi-cave-region series connection are established by simplifying vertical beads-on-string structure. The corresponding mathematical models are developed, in which the flow in fracture regions obey Darcy's law, while the flow in cave regions obeys free flow. Importantly, the gravity is considered due to the flow along vertical direction. Then typical flow regimes are analyzed and sensitivity analysis is conducted.

Our work shows that pressure-derivative curves show similar ‘concave’ characteristic for the cave storage regime and vug/matrix interporosity flow regime. The difference is that the pressure derivative for cave storage regime is unit slope, while this slope is not equal to unity for vug/matrix interporosity flow regime. Therefore, large cave and vug/matrix medium can be distinguished by slope of pressure derivative. More than that, the typical characteristic of vertical beads-on-string structure on type curve is that the cave storage regimes and linear flow regimes alternately appear. This characteristic helps the engineers identify vertical beads-on-string structure. A novel finding is that gravity effect could lead to unit-slope pressure and pressure derivative at late times, which is traditional recognized as the presence of a closed boundary when it may not be the case. Lastly, this methodology is applied to two cases from Shunbei Oilfield in which it is difficult to obtain good fitting quality and interpretation results using traditional methods. Besides conventional properties, the proposed methodology allows us to estimate other properties (e.g., cave height, cave radius), which are not readily obtained from conventional methods.
Integrated Field Management System for LNG Assets: Maximizing Asset Value Through Representative End-To-End Modeling
Authors: Khan Osama Hasan , Ali Samad , Elfeel Mohamed Ahmed , Biniwale Shripad , Dandekar Rashmin
Published: September 2021

Effective asset-level decision-making relies on a sound understanding of the complex sub-components of the hydrocarbon production system, their interactions, along with an overarching evaluation of the asset's economic performance under different operational strategies. This is especially true for the LNG upstream production system, from the reservoir to the LNG export facility, due to the complex constraints imposed by the gas processing and liquefaction plant. The evolution of the production characteristics over the asset lifetime poses a challenge to the continued and efficient operation of the LNG facility. To ensure a competitive landed LNG cost for the customer, the economics of the production system must be optimized, particularly the liquefaction costs which form the bulk of the operating expenditure of the LNG supply chain. Forecasting and optimizing the production of natural gas liquids helps improve the asset economics. The risks due to demand uncertainty must also be assessed when comparing development alternatives.

This paper describes the application of a comprehensive field management framework that can create an integrated virtual asset by coupling reservoir, wells, network, facilities, and economics models and provides an advisory system for efficient asset management. In continuation of previously published work (Khan, Ali, Elfeel, Biniwale, & Dandekar, 2020), this paper focuses on the integration of a steady-state process simulation model that provides high-fidelity thermo-physical property prediction to represent the gas treatment and LNG plant operation. This is accomplished through the Python-enabled extensibility and generic capability of the field management system. This is demonstrated on a complex LNG asset that is fed by sour gas of varying compositions from multiple reservoirs. An asset wide economics model is also incorporated in the integrated model to assess the economic performance and viability of competing strategies.

The impact of changes to the wells and production network system on LNG plant operation is analyzed along with the long-term evolution of the inlet stream specifications. The end-to-end integration enables component tracking throughout the flowing system over time which is useful for contractual and environmental compliance. Integrated economics captures costs at all levels and enables the comparison of development alternatives.

Flexible integration of the dedicated domain models reveals interactions that can be otherwise overlooked. The ability of the integrated field management system to allow the modeling of the sub-systems at the ‘right’ level of fidelity makes the solution versatile and adaptable. In addition, the integration of economics enables the maximization of total asset value by improving decision making.
Model Prediction of Critical Gas Rate with Water-Cut Reversal Effect
Authors: Ekeregbe Merit P.
Published: September 2021

Accurate prediction of gas critical rate is critical to the successful management of gas wells. This paper covers the prediction of gas critical rate and presents limitations of old models with gas condensate wells with water-cut reversal.

Comparison of prediction methods or models with this new method will be explained using field data of condensate wells. The effect and relation of water-cut with critical gas rate determination will be presented and the best method that universally meets changing conditions of the well will be tested with field data.

Any method that must be acceptable must meet the dynamics of the well. No static model can predict accurately a dynamic well and reservoir performance. The old models of critical gas rate prediction show a static outlook, probably see the beginning of the well-life and cannot predict correctly when the fluid phases change in gravity. The late life prediction of the well performance is much more critical than the early life when the well has sufficient energy. The production envelope is more critical at depletion than at when the reservoir energy just kick. Therefore, any model prediction must be dynamic. The results from the old models show that they fail the dynamic test of the well performance. This limitation makes those model unusable in a late life of the well when water cut increases.

This study has revealed a method or a model for critical rate prediction that is accurate throughout the life of the well. The effect of water cut reversal is well tracked with this new model whereas the static nature of other models predicts a wrong minimum rate at a liquid load up rate. The field data reveals that the dynamic nature of well and reservoir performance can only be understood dynamically.
Human Factors and Performance: Reducing Errors and Improving Safety
Authors: Khan Salman , Boyd Leah , Velez Ferdinand
Published: September 2021

While companies employ a variety of tactics to reduce workplace incidents, behavioral-based programs have proven highly effective—particularly because such programs ensure that safety becomes a collective responsibility shared by all employees. However, training managers and their employees on the fundamentals of behavior-based programs such as the Siemens Energy Human Performance (HuP) program has proven challenging during the global COVID-19 pandemic. Training that traditionally was done in person, where people could interact and discuss root causes of accidents in the same room, had to be replaced with virtual training sessions.

This paper reviews the key facets of the HuP program, which includes safety training, raising awareness of employees’ susceptibility to human error, and how to design management systems as well as to promote behaviors to prevent safety incidents. It also reviews common practices in the program—including Stop Work authority, Safety Walk & Talks, daily toolboxes, and rapid risk assessment—and how they are being consolidated into one virtual training curriculum.

People bring their own personal mix of skills, knowledge, experience, attitudes, motivation, habits, and personality to their jobs—and to each task that they routinely perform. The novelty of the HuP approach is that it empowers workers to recognize where errors occur, use the proper tools to change their habits, and then contribute equally to their own safety and operational excellence, rather than relying on written policies and discipline. The efforts focus on safety within and outside the company.
Monitoring Reservoirs from Subsea Wells: Ultra-Long Step out VSP Acquisition Using Enhanced Back-Scattering Fiber
Authors: Roux Pierre-Francois , Barry Alexander , Johnston William , Mead David , Baker Mark R. , Raggio John S.
Published: September 2021

While DAS VSP has become relatively standard in dry-tree applications, acquiring data in subsea wells has remained a technical challenge as umbilical can be tens of kilometers long, thereby reducing the overall quantity of backscattered light to the topside interrogator. This adds to the attenuation due to connectors at the wellhead and along the optical path. Yet, the need for subsea DAS interrogation is high, particularly with the onset of complex, deep-water projects that will require on-demand monitoring capabilities.

In this article, we report on the successful acquisition and subsequent processing of a zero-offset VSP in an ultra-long step-out context. We simulated a subsea well with 69km worth of lead-in fiber to the wellhead, including attenuation at the wellhead mimicking the connectors. The attenuation was tackled by using an active, subsea amplifier (that would normally sit at the wellhead), and an in-house developed engineered fiber that provides a significant uplift in backscattered energy. We acquired this ZVSP both on fiber and with a standard wireline tool string for comparison. The approach presented here combines hardware and processing strategies to tackle the long step-out challenge. We demonstrate the ability to record seismic data even at very large step-out, a requirement for subsea well monitoring.
Assessment of Water Saturation Using Dielectric Permittivity Measurements in Formations with Complex Pore Structure: Application to the Core- and Log- Scale Domains
Authors: Azizoglu Zulkuf , Heidari Zoya , Goncalves Leonardo , Abreu Blanes De Oliveira Lucas , Silva Do Nascimento Neto Moacyr
Published: September 2021

Broadband dielectric dispersion measurements are attractive options for assessment of water-filled pore volume, especially when quantifying salt concentration is challenging. However, conventional models for interpretation of dielectric measurements such as Complex Refractive Index Model (CRIM) and Maxwell Garnett (MG) model require oversimplifying assumptions about pore structure and distribution of constituting fluids/minerals. Therefore, dielectric-based estimates of water saturation are often not reliable in the presence of complex pore structure, rock composition, and rock fabric (i.e., spatial distribution of solid/fluid components). The objectives of this paper are (a) to propose a simple workflow for interpretation of dielectric permittivity measurements in log-scale domain, which takes the impacts of complex pore geometry and distribution of minerals into account, (b) to experimentally verify the reliability of the introduced workflow in the core-scale domain, and (c) to apply the introduced workflow for well-log-based assessment of water saturation.

The dielectric permittivity model includes tortuosity-dependent parameters to honor the complexity of the pore structure and rock fabric for interpretation of broadband dielectric dispersion measurements. We estimate tortuosity-dependent parameters for each rock type from dielectric permittivity measurements conducted on core samples. To verify the reliability of dielectric-based water saturation model, we conduct experimental measurements on core plugs taken from a carbonate formation with complex pore structures. We also introduce a workflow for applying the introduced model to dielectric dispersion well logs for depth-by-depth assessment of water saturation. The tortuosity-dependent parameters in log-scale domain can be estimated either via experimental core-scale calibration, well logs in fully water-saturated zones, or pore-scale evaluation in each rock type. The first approach is adopted in this paper.

We successfully applied the introduced model on core samples and well logs from a pre-salt formation in Santos Basin. In the core-scale domain, the estimated water saturation using the introduced model resulted in an average relative error of less than 11% (compared to gravimetric measurements). The introduced workflow improved water saturation estimates by 91% compared to CRIM. Results confirmed the reliability of the new dielectric model. In application to well logs, we observed significant improvements in water saturation estimates compared to cases where a conventional effective medium model (i.e., CRIM) was used. The documented results from both core-scale and well-log-scale applications of the introduced method emphasize on the importance of honoring pore structure in the interpretation of dielectric measurements.
Scale Buildup Detection and Characterization in Production Wells by Deep Learning Methods
Authors: Cheng Jingru , Mao Deming , Salamah Majid , Horne Roland
Published: September 2021

This study developed an analytical tool for the detection and characterization of scale buildup from well data using deep learning methods. The developed method allows for a sensitive detection of the initiation of scaling as well as an accurate prediction of the magnitude of existing scale buildup. Scale deposition causes tubing ID decreases and therefore results in production declines, so a sensitive approach to detect the scale deposition is valuable to reduce the damage and losses due to this problem.

The underlying deep learning methods are both single- and multioutput, deep neural networks that consist of a combination of convolutional, long short-term memory (LSTM) and fully connected layers. We trained the networks on more than 30 sets of well data, with the objective of predicting the presence and the magnitude of scale. We started with cases of full scale deposition over the whole wellbore depth, and then extended our study to partial depth scale deposition. We built up a point-wise neural network model combining two blocks, which each contain several fully-connected layers followed by an LSTM layer specifically focusing on relatively smaller or larger tubing ID changes, corresponding to more or less scale deposition. To characterize the segmented scale deposition, we transformed to a three-dimensional problem, which can be solved by extracting the tubing ID changes and the scale deposition segment length.

The multioutput model was able to predict the tubing ID and volume changes at the same time, using a combination of convolutional and LSTM layers with residual network blocks and updated using a loss function that we defined. Tubing ID changes were extracted accurately with metric R-square more than 90%, while the length of the scale deposit could be classified into two classes (high scaling or low scaling) with good accuracy.

Though existing physical and chemical methods can be used to analyze scale deposition, the methods are often applied after considerable production decrease has already occurred. By using deep learning algorithms, our study came up with a new way to predict the scaling problem in advance with high sensitivity.
A Comparative Analysis of the Prediction of Gas Condensate Dew Point Pressure Using Advanced Machine Learning Algorithms
Authors: Lertliangchai Thitaree , Dindoruk Birol , Lu Ligang , Yang Xi
Published: September 2021

Dew point pressure (DPP) is a key variable that may be needed to predict the condensate to gas ratio behavior of a reservoir along with some production/completion related issues and calibrate/constrain the EOS models for integrated modeling. However, DPP is a challenging property in terms of its predictability. Recognizing the complexities, we present a state-of-the-art method for DPP prediction using advanced machine learning (ML) techniques. We compare the outcomes of our methodology with that of published empirical correlation-based approaches on two datasets with small sizes and different inputs. Our ML method noticeably outperforms the correlation-based predictors while also showing its flexibility and robustness even with small training datasets provided various classes of fluids are represented within the datasets. We have collected the condensate PVT data from public domain resources and GeoMark RFDBASE containing dew point pressure (the target variable), and the compositional data (mole percentage of each component), temperature, molecular weight (MW), MW and specific gravity (SG) of heptane plus as input variables. Using domain knowledge, before embarking the study, we have extensively checked the measurement quality and the outcomes using statistical techniques. We then apply advanced ML techniques to train predictive models with cross-validation to avoid overfitting the models to the small datasets. We compare our models against the best published DDP predictors with empirical correlation-based techniques. For fair comparisons, the correlation-based predictors are also trained using the underlying datasets. In order to improve the outcomes and using the generalized input data, pseudo-critical properties and artificial proxy features are also employed.
Clustering, Connectivity and Flow in Naturally Fractured Reservoir Analogs
Authors: Sahu Ajay K. , Roy Ankur
Published: September 2021

A previous study by the authors on synthetic fractal-fracture networks showed that lacunarity, a parameter that quantifies scale-dependent clustering in patterns, can be used as a proxy for connectivity and also, is an indicator of fluid flow in such model networks. In this research, we apply the concepts thus developed to the study of fractured reservoir analogs and seek solutions to more practical problems faced by modelers in the oil and gas industry. A set of seven nested fracture networks from the Devonian Sandstone of Hornelen Basin, Norway that have the same fractal-dimension but are mapped at different scales and resolutions is considered. We compare these seven natural fracture maps in terms of their lacunarity and connectivity values to test whether the former is a reasonable indicator of the latter. Additionally, these maps are also flow simulated by implementing a fracture continuum model and using a streamline simulator, TRACE3D. The values of lacunarity, connectivity and fluid recovery thus obtained are pairwise correlated with one another to look for possible relationships. The results indicate that while fracture maps that have the same fractal dimension show almost similar connectivity values, there exist subtle differences such that both the connectivity and clustering values change systematically with the scale at which the fracture networks are mapped. It is further noted that there appears to be a very good correlation between clustering, connectivity, and fluid recovery values for these fracture networks that belong to the same fractal system. The overall results indicate that while the fractal dimension is an important parameter for characterizing a specific type of fracture network geometry, it is the lacunarity or scale-dependent clustering attribute that controls connectivity in fracture maps and hence the flow properties. This research may prove helpful in quickly evaluating connectivity of fracture networks based on the lacunarity parameter. This parameter can therefore, be used for calibrating Discrete Fracture Network (DFN) models with respect to connectivity of reservoir analogs and can possibly replace the fractal dimension which is more commonly used in software that model DFNs. Additionally, while lacunarity has been mostly used for understanding network geometry in terms of clustering, we, for the first time, show how this may be directly used for understanding the potential flow behavior of fracture networks.
A Novel Method of Constructing Spatial Well Pattern for Water Flooding in Fractured-Vuggy Carbonate Reservoirs FVCRs
Authors: Wang Jing , Zhang Tuozheng , Liu Huiqing , Dong Xiaohu , Li Xiaobo , Yang Min , Liu Hongguang , Hu Gaixing , Sepehrnoori Kamy
Published: September 2021

Fractured-vuggy reservoir is significantly different from porous reservoirs. Ordovician reservoir in T Oilfield in China is the largest FVCR around the world. Water flooding has been applied as a prospective method in more than 140 units, but water channeling is especially serious and the recovery is only about 15%. In such reservoir, cavities and fractures are the main storage spaces and flow channels, respectively. Because the fractures and cavities are spatially non-stratified and discretized, the waterflood pattern differs significantly from that in sandstone or porous carbonate reservoirs. It is very essential to construct a spatial well pattern to match the distribution and connectivity of fractures and cavities, which is a very popular topic in recent years.

In this work, we presented a multistage construction method of spatial well pattern combining reservoir engineering with numerical simulation methods. Firstly, the economic concepts of Lorenz curve and Gini coefficient were introduced to choose the injector from all wells to achieve equilibrium displacement of injected water in the plane. Secondly, displacement and drainage equilibrium index (DDEI) was presented to determine the vertical location of the injector to achieve equilibrium displacement and drainage in vertical direction. Thirdly, the vertical locations of the producers were determined based on the distribution of reserves in vertical direction. Fourthly, the local producers were further optimized based on the cavities along the wellbore by numerical simulation. Finally, this method was successfully used to construct the spatial well patterns of fractured-vuggy units with different karst features in A unit of T Oilfield.

The results show that the oil recovery factor is inversely proportional to the Gini coefficient calculated with the combined variable of flow resistance and crude reserve rather than that calculated with flow resistance in pore reservoirs. The ratio of the reserve to formation factor, V/(kh), can be used to determine the vertical location of the injector. And the optimal DDEI which is the ratio of V/(kh) in upper reservoir of the wellbore to that in lower reservoir of the wellbore is equal to 1. The vertical locations of producers are related to the vertical distributions of reserve and cavities in different karst units. At last, the principles of constructing spatial well pattern in fractured-vuggy carbonate reservoirs were proposed.

This work provides an innovative and effective method to establish a spatial well pattern for FVCRs, which will break new ground for efficient development of FVCRs by water flooding.
A Novel Method to Detect Cement through Direct Measurement – Case Histories
Authors: Palisch Terry , Zhang Jeremy
Published: September 2021

Cement quality is typically determined through the use of sonic logging tools, more commonly known as cement bond logs (CBLs), or more recently ultrasonic imaging tools (USITs). In general, these tools have served the industry well over time, but with the advent of new and exotic cement blends, as well as multistage cement jobs in today's unconventional horizontal wells, the quality and even location of the cement has become more problematic for basic CBL/USIT tools to detect. In addition, these tools are ineffective through multiple uncemented casing strings.

A novel method to detect cement was developed as an offshoot of a technology used for detecting proppant in hydraulically fractured wells. This technique uses a non-radioactive tracer which exhibits a high thermal neutron capture cross section that is then incorporated into the proppant grains during manufacture. The proppant can then be detected using standard neutron-logging tools, at any time during the well's life. By incorporating small volumes of this detectible proppant into the cement slurry, the cement can then be detected using the same logging tools. This leads to identification of the top of cement, as well as the cement quality. If desired, the taggant can be staged such that the top and bottom of a cement stage can be detected.

This paper will first review the industry concerns with cement detection. It will then discuss the principles and theory behind how the taggant works, both for basic proppant detection, as well as the novel application as a vehicle for cement detection. This will also include lab testing showing no impact of the tagged proppant on cement performance. The authors will conclude by presenting several case histories of cement detection, including two horizontal well applications, one each in the Permian and Canada. A third case history will also be presented in which the cement was detected through multiple strings of uncemented casing, to verify success of a cement squeeze in a surface casing remediation.

This new technique allows for cement detection in wells in which conventional CBL/USITs are difficult to interpret, including detection of exotic cement blends, and through multiple strings of casing. This allows for more confidence in cement isolation, particularly in today's unconventional wells, where isolation of uphole formations is critical. This paper will be useful for drilling and completion engineers who are concerned with their ability to confirm cement quality, as well as production engineers who must perform remedial cementing operations.
Transport of Polymers in Low Permeability Carbonate Rocks
Authors: Song Haofeng , Ghosh Pinaki , Mohanty Kishore
Published: September 2021

Polymer transport and retention affect oil recovery and economic feasibility of EOR processes. Most studies on polymer transport have focused on sandstones with permeabilities (k) higher than 200 mD. A limited number of studies were conducted in carbonates with k less than 100 mD and very few in the presence of residual oil. In this work, transport of four polymers with different molecular weights (MW) and functional groups are studied in Edwards Yellow outcrop cores (k<50 mD) with and without residual oil saturation (Sor). The retention of polymers was estimated by both the material balance method and the double-bank method. The polymer concentration was measured by both the total organic carbon (TOC) analyzer and the capillary tube rheology. Partially hydrolyzed acrylamide (HPAM) polymers exhibited high retention (> 150 μg/g), inaccessible pore volume (IPV) greater than 7%, and high residual resistance factor (>9). A sulfonated polyacrylamide (AN132), showed low retentions (< 20 μg/g) and low IPV. The residual resistance factor (RRF) of AN132 in the water-saturated rock was less than 2, indicating little blocking of pore throats in these tight rocks. The retention and RRF of the AN132 polymer increased in the presence of residual oil saturation due to partial blocking of the smaller pore throats available for polymer propagation in an oil-wet core.
Connection Recipes: Exploring Optimum Connection Practices through a Data-Driven Approach
Authors: Ashari Rahmat , Sorensen Owen , Ashok Pradeepkumar , van Oort Eric , Isbell Matthew , Chovanetz Koda , Foster Nicole
Published: September 2021

Although numerous studies have investigated how shocks and vibrations contribute to bottomhole assembly (BHA) failures during hole-making, very few have explicitly focused on shock and vibrational behaviors during drillpipe connections. This study adopts a data-driven approach to explore various connection practices and their associated shocks and vibrations, aiming to propose optimum "connection recipes" that minimize negative drillstring impacts during connections.

This study utilized data from surface sensors as well as downhole accelerometers and gyroscopes installed both at a downhole sub and the bit. In total, 520 connections from 5 lateral sections were studied. Several quality checks and corrections were performed to ensure synchronization between surface and downhole data. The analyses focused on two connection phases specifically: going off-bottom and going back to bottom. The presence of stick-slip events and high magnitudes of both maximum and root mean squared (RMS) radial accelerations were examined together with the associated surface drilling parameters. Various visualization approaches were performed to help demonstrate the vibration and shock behaviors resulting from different going off-bottom and going back to bottom practices.

The analyses showed that restarting surface rotational speed at low values (≤ 40 RPM) risks inducing stick-slip events when going back to bottom. When the surface RPM was increased sufficiently, a notable reduction in RMS radial acceleration was observed. Maximum radial acceleration magnitude was highest before WOB application, which could be mitigated by immediate WOB re-application. Appreciable variation in the maximum radial acceleration was apparent when restarting at low (≤ 15 klbf) WOB values. When going off-bottom, drilling off should be accompanied by a reduction in the surface rotational speed to avoid a jump in the maximum radial acceleration values.

This work provides suggestions on how to execute better connections. Since the impacts of shocks and vibrations during connections have previously been largely overlooked, this study fills a knowledge gap to help establish better practices and automation routines to improve the lifespan of the bit and downhole tools.
Trends in the Relationship Between Business and Society: Understanding the Past and Preparing for the Future
Authors: Luca Christiaan , Vencken Marjolijn , van Cranenburgh Katinka , Borbor Juan Diego , Tchilinguirian Anthony
Published: September 2021

How can a business develop sustainable societal relationships in a world that is often described as volatile, uncertain, complex and ambiguous? While public expectations of companies are complicated and continuously changing, there are clear underlying trends in modern society that shape this relationship. Understanding these trends allows a company to develop the capability of proactively managing societal relationships. The findings of the study are especially relevant for industries that visibly operate in the public space and should anticipate societal resistance.

This paper first describes the societal trends that shaped the way people and organizations have interacted since the Second World War. In this period increasing environmental and social awareness and assertiveness developed along three evolutionary paths: –active public discourse that continuously pushes the boundaries of what is deemed acceptable and desirable, with increasing focus on human values and space for the individual;–industry sectors and international organizations that try to preempt new societal expectations with voluntary guidelines and self-regulation; and–governments that formalize important and matured parts of the public discourse and voluntary guidelines in laws and regulations.

Secondly, the authors adopt a practical model to describe how companies have struggled to keep up with this continuously evolving and dynamic societal landscape due to lack of adaptation. An increasingly defensive and reactive business approach to societal pressure has led to a low point in trust from stakeholders. To regain trust and their social license to operate companies need to take a more proactive approach to societal relationships, which require both organizational and cultural change.

Finally, the authors take the example from the safety journey, where the oil and gas industry has been very successful in demonstrating that excellence in safety is both a moral obligation and good for the bottom line. The same applies to excellence in societal relationships. Using the evolutionary model of safety culture with its maturity ladder as analogue, the authors provide a practical and value-driven framework to guide companies on their organizational and cultural change journey towards effective societal relationship management.
Industry First Openhole Alternate Path Gravel Pack Completion in HPHT Environment: Fluid Development and Case History
Authors: Pillai Pradeep , Lin Chih-Cheng , Brege Jonathan , Mohan Raj , Derkach Edward , Lafitte Valerie , Gadiyar Bala , Aung Tint Htoo , Santamaria Juan-Carlos
Published: September 2021

For offshore wells requiring sand control, Open Hole Gravel Packing (OHGP) with or without shunted screen technology is a common completion technique. Prior to this paper, there has been no application of shunted screen OHGP in High-Pressure High-Temperature (HPHT) environment due to lack of a viscous fluid availability in high-density divalent brines (> 14.6 lbm/U.S. gal).

For the case study, the fluid requirements in terms of density and temperature were 15.4 lbm/U.S. gal and 265°F, respectively. The only brine option at this density was a blend of calcium chloride/bromide and zinc bromide. In this brine and density, none of the existing fluids work hence a novel polymer-based fluid had to be developed. The fluid had to pass the following tests a) rheology before and after subjecting to high shear of both uncontaminated and contaminated fluids at 3 different temperatures b) sand settling tests at 4 different temperatures c) Production Screen Tester to ensure the fluid does not plug the screens during the job. System Integration Tests (SIT) were performed to ensure the mixing equipment would be able to batch mix the fluid and actual pumping equipment would function properly with the fluid. The field trial planning included simulations, pre-job meetings, and fluid management plan. The job was executed as per the procedure outlined during pre-job meetings.

This paper discusses laboratory development, yard test qualification, and successful shunted screen OHGP case history of a novel 15.4 lbm/U.S. gal viscous gravel pack carrier fluid.
An Efficient Deep Learning-Based Workflow Incorporating a Reduced Physics Model for Subsurface Imaging in Unconventional Reservoirs
Authors: Onishi Tsubasa , Chen Hongquan , Datta-Gupta Akhil , Mishra Srikanta
Published: September 2021

We present a novel deep learning-based workflow incorporating a reduced physics model that can efficiently visualize well drainage volume and pressure front propagation in unconventional reservoirs in near real-time. The visualizations can be readily used for qualitative and quantitative characterization and forecasting of unconventional reservoirs.

Our aim is to develop an efficient workflow that allows us to ‘see’ within the subsurface given measured data, such as production data. The most simplistic way to achieve the goal will be to merely train a deep learning-based regression model where the input consists of some measured data, and the output is a subsurface image, such as pressure field. However, the high output dimension that corresponds to spatio-temporal steps makes the training inefficient. To address this challenge, an autoencoder network is applied to discover lower dimensional latent variables that represent high dimensional output images. In our approach, the regression model is trained to predict latent variables, instead of directly constructing an image. In the prediction step, the trained regression model first predicts latent variables given measured data, then the latent variables will be used as inputs of the trained decoder to generate a subsurface image. In addition, fast marching-method (FMM)-based rapid simulation workflow which transforms original 2D or 3D problems into 1D problems is used in place of full-physics simulation to efficiently generate datasets for training. The capability of the FMM-based rapid simulation allows us to generate sufficient datasets within realistic simulation times, even for field scale applications.

We first demonstrate the proposed approach using a simple illustrative example. Next, the approach is applied to a field scale reservoir model built after the publicly available data on the Hydraulic Fracturing Test Site-I (HFTS-I), which is sufficiently complex to demonstrate the power and efficacy of the approach. We will further demonstrate the utility of the approach to account for subsurface uncertainty. Our approach, for the first time, allows data-driven visualization of unconventional well drainage volume in 3D. The novelty of our approach is the framework which combines the strengths of deep learning-based models and the FMM-based rapid simulation. The workflow has flexibility to incorporate various spatial and temporal data types.