bluuebunny/crossref_metadata_2025 huggingface dataset:
A Comprehensive Review and Status of Renewable Resources and Oil & Gas Under the Supply and Demand Dynamics in the World
Authors: Canbaz Celal Hakan , Aydin Hakki , Canbaz Ezgi , Akberov Ismayil , Aksahan Firat , Hussain Athar , Emadi Hossein , Temizel Cenk
Published: October 2021

As major oil and gas companies have been investing in renewable energy, renewable energy has been part of the oil and gas industry in the last decade. Originally, renewables were seen as a competing form of energy source as a threat that may replace or decrease the share of fossil fuels as an alternative energy resource in the US and developed countries. However, oil and gas industry has adapted to the wind of change and has started investing and utilizing the renewable sources of energy significantly. In this perspective, this study investigates and outlines the latest advances, technologies, potential of renewables both as an alternative and a complementary source of energy in the world n the current supply and demand dynamics of oil and gas resources.

A comprehensive literature review focusing on the recent developments and findings in the renewable resources along with the availability of the renewable energy and locations are outlined and discussed under the current dynamics of the oil and gas market and resources. Literature review includes a broad spectrum that spans from technical petroleum literature with very comprehensive research using SCOPUS database to non-technical but renowned resources including journals and other publications including raw data as well as forecasts and opinions of respected experts. The raw data and expert opinions are organized, summarized and outlined in a temporal way within its category for the respective energy source.

Not only the facts and information are outlined for the individual type of energy resource but also the relationship between the forms of energy resources are discussed from a perspective of their roles either as a competing or a complementary source to oil and gas. In this sense, this study goes beyond only providing raw data or facts about the energy resources but also a thorough publication that provides the oil and gas industry professional with a clear image of the past, present and the expected near future of the oil and gas industry as it stands with respect to renewable energy resources.

Among the few existing studies that shed light on the current status of the oil and gas industry facing the development of the renewable energy are up-to-date and the existing studies within SPE domain focus on facts only lacking the interrelationship between the individual form of renewable energy and oil and gas such as solar energy used in oil and gas fields as a complementary renewable energy.
The Key Factors of Low-Frequency Electric Heating Assisted Depressurization Method in the Exploiting of Methane Hydrate Sediments
Authors: Zhao Ermeng , Hou Jian , Ji Yunkai , Liu Lu , Liu Yongge , Bai Yajie
Published: October 2021

Natural gas hydrate is widely distributed in the permafrost and marine deposits, and is regarded as an energy resource with great potential. The low-frequency electric heating assisted depressurization (LF-EHAD) has been proven to be an efficient method for exploiting hydrate sediments, which involves complex multi-physics processes, i.e. current conduction, multiphase flow, chemical reaction and heat transfer. The physical properties vary greatly in different hydrate sediments, which may profoundly affect the hydrate decomposition in the LF-EHAD process.

In order to evaluate the influence of hydrate-bearing sediment properties on the gas production behavior and energy utilization efficiency of the LF-EHAD method, a geological model was first established based on the data of hydrate sediments in the Shenhu Area. Then, the influence of permeability, porosity, thermal conductivity, specific heat capacity, hydrate saturation and hydrate-bearing layer (HBL) thickness on gas production behavior is comprehensively analyzed by numerical simulation method. Finally, the energy efficiency ratio under different sediment properties is compared.

Results indicate that higher gas production is obtained in the high-permeability hydrate sediments during depressurization. However, after the electric heating is implemented, the gas production first increases and then tends to be insensitive as the permeability decreases. With the increasing of porosity, the gas production during depressurization decreases due to the low effective permeability; while in the electric heating stage, this effect is reversed. High thermal conductivity is beneficial to enhance the heat conduction, thus promoting the hydrate decomposition. During depressurization, the gas production is enhanced with the increase of specific heat capacity. However, more heat is consumed to increase the reservoir temperature during electric heating, thereby reducing the gas production. High hydrate saturation is not conducive to depressurization because of the low effective permeability. After electric heating, the gas production increases significantly. High HBL thickness results in a higher gas production during depressurization, while in the electric heating stage, the gas production first increases and then remains unchanged with the increase of thickness, due to the limited heat supply. The comparison results of energy efficiency suggest that electric heating is more advantageous for hydrate sediments with low permeability, high porosity, high thermal conductivity, low specific heat capacity, high hydrate saturation and high HBL thickness. The findings in this work can provide a useful reference for evaluating the application of the LF-EHAD method in gas hydrate sediments.
The Application of Petroleum Geochemical Methods to Production Allocation of Commingled Fluids
Authors: Patience Richard , Bastow Mark , Fowler Martin , Moore Julian , Barrie Craig
Published: October 2021

Production allocation from petroleum geochemistry is defined here as the quantitative determination of the amount or portion of a commingled fluid to be assigned to two or more individual fluid sources (e.g., a pipeline, field, reservoir, well) at a particular moment in time, based on the fluid chemistry. It requires: i) knowledge of the original chemical compositions of each of the fluids prior to mixing (referred to here as the "end members"), and ii) that statistically valid differences in their chemistries can be identified. Petroleum geochemical-based methods for production monitoring and allocation are much lower cost than using production logging tools, as there is no additional rig time or extra personnel required at the well site. Additionally, no intervention to the production of hydrocarbons from a well is required and, hence, there is none of the risk entailed in additional operational activity. Geochemical methods are applicable to a wide range of fields, irrespective of pressure, temperature, reservoir quality and reservoir fluid type. The method has been in existence for over 30 years, during which time a number of different analytical methods, data pre-processing and treatment approaches have been applied. This paper summarises these approaches, and provides examples, but also describes a "best practice" which is not a "one size fits all" approach, as is sometimes seen in the literature. A successful production allocation study consists of the following steps: i) Selection of end member samples that contribute to the commingled production fluid; ii) Determination of the differences in chemical composition of the end members through laboratory analysis of the end members (e.g. by WO-GC), replicate analyses of samples and statistical treatment of the data (e.g. PCA); iii) If statistically significant differences exist, laboratory analysis of the end members and commingled fluids with appropriate replicate analyses of samples; iv) Data selection, pre-processing (e.g. selection of ratios or concentrations of components); v) Determination of end member contributions by solving equations (e.g. least squares best fit) and uncertainty estimation (e.g. Monte Carlo or Bootstrap methods). The differences in approach for conventional versus unconventional plays are also discussed.
Optimization Approach for Determination of the Dosage of Diversion Agents in Temporarily Plugging Fracturing of Shale Using 3D Printing Fractures
Authors: Wang Meng , Che Mingguang , Wang Guangyao , Wang Yonghui , Zhu Guangyou
Published: October 2021

Application of diversion agents in temporarily plugging fracturing of horizontal wells of shale has becoming more and more popular. Nevertheless, the studies on determining the diverter dosage are below adequacy. A novel approach based on laboratory experiments, logging data, rock mechanics tests and fracture simulation was proposed to optimizing the dosage of diversion agents. The optimization model is based on the classic Darcy Law. A pair of 3D-printed rock plates with rugged faces was combined to simulate the coarse hydraulic fractures with the width of 2.0 ~ 7.0 mm. The mixture of the diversion agents and slickwater was dynamically injected to the simulated fracture in Temco fracture conductivity system to mimic the practical treatment of temporarily plugging fracturing. The permeability of the temporary plugging zone in the 3D-printed fractures was measured in order to optimize the dosage of the selected diversion agents. The value of Pnet (also the value of ΔP in Darcy Formula) required for creation of new branched fractures was determined using the Warpinski-Teufel Failure Rules. The hydraulic fractures of target stages were simulated to obtain the widths and heights. The experimental results proved that the selected suite of the diversion agents can temporarily plug the 3D-printed fractures of 2.0 ~ 7.0 mm with blocking pressure up to 15 MPa. The measured permeability of the resulting plugging zones was 0.724 ~ 0.933 D (averaging 0.837 D). The value of Pnet required for creation of branched fractures in shale of WY area (main shale gas payzone of China) was determined as 0.4 ~ 15.6 MPa (averaging 7.9 MPa) which means the natural fractures and/or weak planes with approaching angle less than 70o could be opened to increase the SRV. The typical dosage of the diversion agents used for one stage of the horizontal wells was calculated as 232 ~ 310 kg. The optimization method was applied to the design job of temporarily plugging fracturing of two shale gas wells. The observed surface pressure rise after injection of diversion agents was 0.6 ~ 11.7 MPa (averaging 4.7 MPa) and the monitored microseismic events of the test stages were 37% more than those of the offset stages.
Classification and Localization of Low-Frequency DAS Strain Rate Patterns with Convolutional Neural Networks
Authors: Chen Mengyuan , Tang Jin , Zhu Ding , Daniel Hill Alfred
Published: October 2021

Distributed acoustic sensing (DAS) has been used in the oil and gas industry as an advanced technology for surveillance and diagnostics. Operators use DAS to monitor hydraulic fracturing activities, to examine well stimulation efficacy, and to estimate complex fracture system geometries. Particularly, low-frequency DAS can detect geomechanical events such as fracture-hits as hydraulic fractures propagate and create strain rate variations. Analysis of DAS data today is mostly done post-job and subject to interpretation methods. However, the continuous and dense data stream generated live by DAS offers the opportunity for more efficient and accurate real-time data-driven analysis. The objective of this study is to develop a machine learning-based workflow that can identify and locate fracture-hit events in simulated strain rate response that is correlated with low-frequency DAS data. In this paper, "fracture-hit" refers to a hydraulic fracture originated from a stimulated well intersecting an offset well. We start with building a single fracture propagation model to produce strain rate patterns observed at a hypothetical monitoring well. This model is then used to generate two sets of strain rate responses with one set containing fracture-hit events. The labeled synthetic data are then used to train a custom convolutional neural network (CNN) model for identifying the presence of fracture-hit events. The same model is trained again for locating the event with the output layer of the model replaced with linear units. We achieved near-perfect predictions for both event classification and localization. These promising results prove the feasibility of using CNN for real-time event detection from fiber optic sensing data. Additionally, we used image analysis techniques, including edge detection, for recognizing fracture-hit event patterns in strain rate images. The accuracy is also plausible, but edge detection is more dependent on image quality, hence less robust compared to CNN models. This comparison further supports the need for CNN applications in image-based real-time fiber optic sensing event detection.
Advanced Fracturing Design Simulator-Assisted Modeling Coupled with Application of Enhanced Stimulation Fluids Raises Performance of Acid Fractured Wells
Authors: Kalabayev Ruslan , Abdrazakov Dmitriy , Juldugulov Yeltay , Stepanov Vladimir , Emelyanov Denis , Romanovskii Rostislav , Kinnear Campbell
Published: October 2021

Important factors affecting acid fracturing efficiency include etched fracture geometry, cleanup, and optimum differential etching to retain open channels after fracture closure. A recently applied integrated approach combined improvements in all three factors: new fracture simulation techniques enabled fracture geometry optimization, single-phase retarded acid provided significant increase in half-length, and high retained permeability viscous fluids supported better fracture cleanup. The approach was successfully implemented in several carbonate oil fields and resulted in a substantial productivity index increase.

The approach enables acid fracture optimization in three steps. First, the high retained permeability, low-pH pad fluids and polymer-free leakoff control acids are used in combination to enhance formation cleanup after a treatment and to reduce the concentration of polymers in fissures network of naturally fractured carbonate reservoirs. Second, a new single-phase retarded acid is used to achieve longer half-length due to retarded reaction with formation rock and favorable viscous fingering effects. Third, a new acid fracturing simulation model is used to optimize fracture geometry. The simulation technique employs an innovative transport model that includes the viscous fingering effect, advanced leakoff simulation, changing acid rheology upon spending, and a novel calculation approach to mixed fluids' rheology.

This combined concept was applied during acid fracturing treatments in moderate permeability wells of carbonate reservoirs with target intervals up to 4,600 m TVD and temperatures up to 125°C. The treatments consisted of guar-free low-pH pad fluid, polymer-free leakoff control acid, and single-phase retarded acid. Treatment optimization was performed using an advanced acid fracturing simulator to properly address the transport processes within the fracture in a low-stress-contrast environment. After the treatments, the pressure transient analysis indicated a strong linear regime for more than 15 hours, indicating effective fracture half-length at least 25% higher than average half-length after acid fracturing in offset wells where the conventional approach had been applied. Post-treatment half-length calculations showed a good match with advanced simulator results and proved the importance of accounting for viscous fingering effects during acid fracture half-length calculations. Calculation of the productivity index from the production data showed at least 15% increase compared to conventional acid fracturing treatments. The post-fracturing production decline rate was at least 20% slower than that of the conventional treatment in offset wells, which can be explained by the longer conductive fracture.
Development of Time Lapse VSP Integration Workflow: A Case Study at Farnsworth CO2-EOR Project
Authors: McMillan Marcia , Will Robert , Bratton Tom , Ampomah William , Khaniani Hassan
Published: October 2021

This study aims to develop a 4D Vertical Seismic Profile (VSP) integration workflow to improve the prediction of subsurface stress changes. The selected study site is a 5-spot pattern within the ongoing CO2-EOR operations at the Farnsworth Field Unit FWU in Ochiltree County, Texas. The specific pattern has undergone extensive geological and geomechanical characterization through the acquisition of 3D seismic data, geophysical well logs, and core.

This workflow constrains a numerical hydromechanical model by applying a penalty function formed between "modeled" versus "observed" time-lapse compressional and shear seismic velocity changes. Analyses of geophysical logs and ultra-sonic measurements on core exhibit measurable sensitivities to changes in both fluid saturation and mean effective stress. These data are used to develop a site-specific rock physics model and stress-velocity relationship, which inform the numerical models used to generate the "modeled" portion of the penalty function. The "observed" portion of the penalty function is provided by a novel elastic full-waveform inversion of the available 3D baseline and three monitor surveys to produce high-quality estimates of time-lapse compressional and shear seismic velocity changes.

The modeling workflow accounts sequentially for fluid substitution and stress impacts. Hydrodynamic and geomechanical properties of the 3D coupled numerical model are estimated through geostatistical integration of well log and core data with 3D seismic inversion products. Changes in seismic velocities due to fluid substitution are computed using the Biot-Gassmann workflow and site-specific rock physics. Stress impacts on time-lapse seismic velocity changes are modeled from the effective stress output of the hydromechanical model and are initially based on the velocity versus effective stress relationship extracted from core mechanical testing.

Based on the principle of superposition of seismic wavefields, seismic velocity changes attributed to fluid substitution and that due to changes in mean effective stress are treated as linearly additive. The modeled results are upscaled using Backus averaging to reconcile scale discrepancies between the modeled and measured datasets to formulate the penalty function.

This manuscript presents the forward modeling process and concludes that for the base case, the seismic velocity changes due to mean effective stress dominates over the seismic velocity changes attributed to fluid substitution because of the extensive range of the pressure perturbations. Successful minimization of this penalty function calibrates the coupled hydrodynamic geomechanical numerical model and affirms the suitability of acoustic time-lapse measurements such as 4D-VSP for geomechanical calibration.
Integrated Acid Fracture Model with Reservoir Simulation Under Non-Isothermal Condition
Authors: Tajima Tohoko , Daniel Hill A. , Zhu Ding
Published: October 2021

Modeling of acid fracturing process is challenging because of the coupled complex effects of flow through porous media and fractures, chemical reaction in a geostatistical base, wormhole propagation, and reservoir heterogeneity. To avoid the complexity, decoupled approaches are commonly used; the reservoir effect is represented by leakoff with a constant leakoff coefficient, and analytical solutions for heat flux from a reservoir is used to avoid complexity. An acid fracturing numerical model is presented that is coupled with a single-phase black oil reservoir simulator for a vertical well in the carbonate reservoir. The coupled acid fracturing model considers fracture propagation, acid transport, and heat transfer. After simulating acid fracturing, the conductivity of the fracture is calculated using empirical correlations, and the productivity is computed by simulating the flow to the well. Non-isothermal condition is assumed to simulate the flow in both the fracture and reservoir because the acid reaction is temperature sensitive. Leakoff from fracture to reservoir is simulated with a reservoir flow model for pressure and leakoff velocity as functions of time and location. Wormhole propagation from the fracture is considered by using empirical equations for wormhole propagation based on leakoff velocity estimated from the reservoir simulation.

The benefits of coupled modeling are evaluated by comparing the conventional acid fracturing model which uses a decoupled approach to the numerical acid fracturing model developed in this study. The results show that the coupling reservoir model improves the accuracy of estimated in fracture conductivity. It has been shown that the analytical equations for heat from a reservoir used in literature overestimates the final acid fracture conductivity. Thus, it is suggested to use fully numerically solve fluid flow and energy balance in a fracture and a reservoir.

Complex leakoff due to pressure and temperature change with time and wormhole propagation was implemented in the simulator. The wormhole effect was added and the distribution of leakoff coefficient was reasonable. A comparison of simulation results with and without wormholes showed that the significant difference was not observed in acid concentration, but ideal width distribution was lower with wormholes. It is concluded based on the observation of the study that the leakoff from acid fracture represented by a reservoir model with wormhole propagation is important to correctly understand acid fracture efficiency. Simply using a constant leakoff coefficient can lead to significant error and misleading conclusions.
The Use of Controlled Dissolution Glass for the Consolidation of a High Porosity Chalk
Authors: Olsen Max , Hatlebakk Ragni , Holcroft Chris , Flatebø Roar Egil , Hoq Asif , Stavland Arne , Giske Nils Harald
Published: October 2021

This paper reports the development and testing, of a Phosphate controlled dissolution glass composition used to strengthen the matrix of chalk whilst retaining the permeability of the rock, facilitating improved hydrocarbon recovery in unstable wells.

Multiple versions of the glass solutions and different types of colloidal silica were extensively tested in the laboratory to determine injectability and reactivity with calcium carbonate rocks. The goal of the testing was to determine the best performing solution for use in a field trial in the Norwegian North Sea. The laboratory testing included filtration and core flood tests to determine the injectability of the solutions and post treatment permeability, and Brazilian strength tests to determine the tensile strength of the treated chalk cores. The filterability was tested through filter screen sizes ranging from 5 to 0.6 µm. Core flood testing was performed on 10 cm long chalk cores with 1.5 mD permeability.

The glass solutions showed the best results in the filtration and core flood testing, achieving significantly greater invasion depth than any of the colloidal silica samples. The phosphate glass treated chalk cores maintained 70 to 100% of the original permeability while delivering a 3 to 5 fold tensile strength increase. The lab tests demonstrated the potential of a glass based treatment to strengthen chalk formations without impeding permeability.Based on the promising results from the lab tests, it was decided to trial the selected glass solution in a mature vertical proppant fractured well. The test confirmed that the glass solution could be pumped into the well, but the test failed pre-maturely after two months of varied production, and the trial will not be covered in this paper.However, due to the high value in being able to stabilize chalk in the field, the Operator is evaluating a new trial in a horizontal well, and learnings from the first trial will be used to inform further lab tests in the next phase.

The glass solution used in this trial is being further developed to be used in other formation types, such as sand and non-calcium containing reservoirs.
Indirect Estimation of Clastic Reservoir Rock Grain Size from Wireline Logs Using a Supervised Nearest Neighbor Algorithm: Preliminary Results
Authors: Anifowose Fatai Adesina , Mezghani Mokhles Mustafa , Shahrani Saeed Saad
Published: October 2021

Reservoir rock textural properties such as grain size are typically estimated by direct visual observation of the physical texture of core samples. Grain size is one of the important inputs to petrophysical characterization, sedimentological facies classification, identification of depositional environments, and saturation models. A continuous log of grain size distribution over targeted reservoir sections is usually required for these applications. Core descriptions are typically not available over an entire targeted reservoir section. Physical core data may also be damaged during retrieval or due to plugging.

Alternative methods proposed in literature are not sustainable due to their limitations in terms of input data requirements and inflexibility to apply them in environments with different geological settings. This paper presents the preliminary results of our investigation of a new methodology based on machine learning technology to complement and enhance the traditional core description and the alternative methods. We developed and optimized supervised machine learning models comprising K-nearest neighbor (KNN), support vector machines (SVM), and decision tree (DT) to indirectly estimate reservoir rock grain size for a new well or targeted reservoir sections from historical wireline logs and archival core descriptions.

We used anonymized datasets consisting of nine wells from a clastic reservoir. Seven of the wells were used to train and optimize the models while the remaining two were reserved for validation. The grain size types range from clay to pebbles. The performance of the models confirmed the feasibility of this approach. The KNN, SVM, and DT models demonstrated the capability to estimate the grain size for the test wells by matching actual data with a minimum of 60% and close to 80% accuracy. This is an accomplishment taking into account the uncertainties inherent in the core analysis data. Further analysis of the results showed that the KNN model is the most accurate in performance compared to the other models.

For future studies, we will explore more advanced classification algorithms and implement new class labeling strategies to improve the accuracy of this methodology. The attainment of this objective will further help to handle the complexity in the grain size estimation challenge and reduce the current turnaround time for core description.
Development of a Gas Flow Rate Model for Multi-Stage Choke System in HPHT Gas Wells Using Artificial Intelligence
Authors: alghamdi Ahmed Abdullah , Almutairi Nawaf Saud , Muslim Ali , Khaldi Humoud , Abdulraheem Abdulazeez
Published: October 2021

Objective/Scope

Accurate well production rate measurement is critical for reservoir management. The production rate measurement is carried out using surface devices, such as orifice flow meter and venturi flow meter. For large offshore fields development with a high number of wells, the installation and maintenance costs of these flowmeters can be significant. Therefore, an alternative solution needs to be developed. This paper described the successful implementation of Artificial Intelligence in predicting the production rate of big-bore gas wells in an offshore field.

Methods, Procedures, Process

Successful application of AI depends on capitalizing on a large set of data. Therefore, flowing parameters data were collected for more than 30 gas wells and totaling over 100,000 data points. These wells are producing gas with slight solid production from a high-pressure high-temperature field. In addition, these wells are equipped with a multistage choke that reduces the noise and vibration levels. An Artificial Neural Network is trained on the data using Gradient Descent method as the optimization algorithm. The network takes as an input the upstream and downstream pressure and temperature, and the choke size. The output is the gas rate measured in MMscf/day.

Results, Observations, Conclusions

The data set was divided into 70% for training the neural network and 30% for validation. Artificial Neural Network (ANN) was used and the developed model compared exceptionally well with the gas rates measured from the calibrated venturi meters. The gas rate estimation was within a 5% error. The model was developed for two types of completions: 7" and 9-5/8" production tubing. One of the challenges was how to estimate the choke wear which plays a major role in the quality of the choke size data. A linear choke wear deterioration is applied in this case, while work in progress is taking place for acquiring acoustic data that can significantly improve the choke wear modeling.

Novel/Additive Information

The novel approach presented in this paper capitalizes on Al analytics for estimating accurate gas flow rate values. This approach has improved the reservoir data management by providing accurate production rate values which has drastically improved the reservoir simulation. Moreover, the robustness of the AI model has forced us to rethink the conventional design of installing a flow meter for every well. As shown in this paper, the AI model served as an alternative to conventional venturi meters. We believe that the application of AI models to other aspects of production surveillance will lead to a shift into how operators design production facilities.
Modeling and Analysis of Diagnostic Fracture Injection Tests DFITs
Authors: Bakar Recep , Ozkan Erdal , Kazemi Hossein
Published: October 2021

Diagnostic fracture injection tests (DFIT) are used as an indirect method to determine closure pressure and formation effective permeability in unconventional reservoirs as a first step in formation evaluation. The information obtained from DFIT is particularly useful because it is obtained before any production for a given well is available. In DFIT, a small fracture is created by injecting few barrels of completion fluid until formation breaks down and a fracture is initiated and propagates a short distance into the reservoir. Then, injection is stopped, and the pressure decline (or falloff) is monitored. From this pressure decline, the effective permeability of the formation is estimated by Nolte's G-function, log-log plot, or square root of time analysis. In this research, the viability of the common DFIT analysis techniques was investigated for unconventional reservoirs with and without micro-fractures by using a numerical hydraulic fracturing simulator, CFRAC. The results of numerical simulations were investigated to assess the impact of permeability, residual fracture aperture, and complex fracture networks on conventional DFIT interpretations. For the example considered in this work, the commonly used G-function analysis yielded estimates of permeability over an order of magnitude higher than the simulated matrix permeability. Error in the G-function estimates of permeability were higher for higher matrix permeability and in the existence of a fracture network. On the other hand, straight-line analysis of Ap versus G-time yielded much closer (in the same order of magnitude) estimates of permeability.
From Biomedical to Oil Industry: Promising Mesoporous Materials for Oil Field Applications
Authors: Alsmaeil Ahmed Wasel , Hammami Mohamed Amen , Abdel-Fattah Amr Ismail , Kanj Mazin Yousef , Giannelis Emmanuel P
Published: October 2021

Developing nanocarriers deliver molecules to targeted locations has received widespread attention in different fields ranging from biomedical to oil and gas industries. Mesoporous Silica Nanoparticles (MSNs), where the pore size diameter ranges from 2-50 nm, have become attractive in many fields including biomedicine. One advantage is the ability to control the size, morphology of the particles, and the internal and external surfaces properties which enable encapsulating molecules of different size and charges. Moreover, it is possible to functionalize the pores and the surface of the MSNs, which make them suitable to host different molecules and release them in situ in a controlled manner. Despite the numerous studies of MSNs, little has been devoted to subsurface applications. This review will highlight some of the interesting characteristics of MSNs that make them promising carriers of molecules for slow and/or stimuli-responsive delivery for oil field applications. For example, they could be utilized for the controlled release of surfactants for enhanced oil recovery applications to minimize surfactant losses near the well-bore area. The mesoporous materials can be designed to harvest the ions normally present in oil field water, and the high temperatures encountered when travelling deep in the reservoir to release the surfactant. The ion exchange process makes it possible to engineer the MSNs to release their cargo for efficient and stimuli responsive delivery applications. The ion-responsive release was analyzed by the interfacial tension behavior between crude oil and high salinity water (HSW). It is concluded that the interfacial tension could be reduced up to 0.0045 mN/m when the mesoporous silica particles are suspended in HSW in comparison to 0.9 mN/m when suspended in DI water.
Characterization of Fluid Drainage Mechanism at Core and Pore Scales: an NMR Capillary Pressure–Based Saturation Exponent Prediction
Authors: Isah Abubakar , Adebayo Abdulrauf Rasheed , Mahmoud Mohamed , Babalola Lamidi O. , El-Husseiny Ammar
Published: October 2021

Capillary pressure (Pc) and electrical resistivity index (RI) curves are used in many reservoir engineering applications. Drainage capillary pressure curve represents a scenario where a non-wetting phase displaces a wetting phase such as (i) during gas injection (ii) gas storage in reservoirs (e.g. aquifer or depleted hydrocarbon reservoirs). The gas used for injection is typically natural gas, N2, or CO2. Gas storage principally used to meet requirement variations, and water injection into oil-wet reservoirs are drainage processes. Resistivity index (RI) curve which is used to evaluate the potential of oil recovery from a reservoir, is also an important tool used in log calibration and reservoir fluid typing. The pore drainage mechanism in a multimodal pore system is important for effective recovery of hydrocarbon reserves; enhance oil recovery (EOR) planning and underground gas storage. The understanding of pore structure and drainage mechanism within a multimodal pore system during petrophysical analysis is of paramount importance to reservoir engineers. Therefore, it becomes inherent to study and establish a way to relate these special core analyses laboratory (SCAL) methods with quick measurements such as the nuclear magnetic resonance (NMR) to reduce the time requirement for analysis. This research employed the use of nuclear magnetic resonance (NMR) to estimate saturation exponent (n) of rocks using nitrogen as the displacing fluid. Different rock types were used in this study that cover carbonates, sandstones, and dolomites. We developed an analytical workflow to separate the capillary pressure curve into capillary pressure curve for macropores and a capillary pressure curve for the micropores, and then used these pore scale Pc curves to estimate an NMR - capillary pressure - based electrical resistivity index - saturation (NMR-RI-Sw) curve for the rocks. We predicted the saturation exponent (n) for the rock samples from the NMR-RI-Sw curve. The NMR-based saturation exponent estimation method requires the transverse (T2) relaxation distribution of the rock - fluid system at various saturations. To verify the reliability of the new workflow, we performed porous plate capillary pressure and electrical resistivity measurements on the rock samples. The reliability of the results for the resistivity index curve and the saturation exponent was verified using the experimental data obtained from the SCAL method. The pore scale Pc curve was used to ascertain the drainage pattern and fluid contribution of the different pore subsystems. For bimodal rock system, the drainage mechanism can be in series, in parallel, or in series - parallel depending on the rock pore structure.
Thermal Effects on Far-Field Distributed Acoustic Strain-Rate Sensors
Authors: Leggett Smith Edward , Zhu Ding , Hill Alfred Daniel
Published: October 2021

Fiber-optic cables cemented outside of the casing of an unconventional well measure cross-well strain changes during fracturing of neighboring wells with low-frequency distributed acoustic sensing (LF-DAS). As a hydraulic fracture intersects an observation well instrumented with fiber-optic cables, fracture fluid injected at ambient temperatures can cool a section of the sensing fiber. Often, LF-DAS and distributed temperature sensing (DTS) cables are run in tandem, enabling the detection of such cooling events. The increasing use of LF-DAS for characterizing unconventional hydraulic fracture completions demands an investigation of the effects of temperature on the measured strain response by LF-DAS. Researchers have demonstrated that LF-DAS can be used to extract the temporal derivative of temperature for use as a differential-temperature-gradient sensor. However, differential-temperature-gradient sensing is predicated on the ability to filter strain components out of the optical signal.

In this work, beginning with an equation for optical phase shift of LF-DAS signals, a model relating strain, temperature, and optical phase shift is explicitly developed. The formula provides insights into the relative strength of strain and temperature effects on the phase shift. The uncertainty in the strain-rate measurements due to thermal effects is estimated. The relationship can also be used to quantify uncertainties in differential-temperature-gradient sensors due to strain perturbations. Additionally, a workflow is presented to simulate the LF-DAS response accounting for both strain and temperature effects. Hydraulic fracture geometries are generated with a 3D fracture simulator for a multi-stage unconventional completion. The fracture width distributions are imported by a displacement discontinuity method program to compute the strain-rates along an observation well. An analytic model is used to approximate the temperature in the fracture. Using the derived formulae for optical phase shift, the model outputs are then used to compute the LF-DAS response at a fiber-optic cable, enabling the generation of waterfall plots including both strain and thermal effects.

The model results suggest that before, during, and immediately following a fracture intersecting a well instrumented with fiber, the strain on the fiber drives the LF-DAS signal. However, at later times, as completion fluid cools the observation well, the temperature component of the LF-DAS signal can equal or exceed the strain component. The modeled results are compared to a published field case in an attempt to enhance interpretation of LF-DAS waterfall plots. Finally, we propose a sensing configuration in order to identify the events when "wet fractures" (fractures with fluids) intersect the observation well.
Fast Upscaling of Polymer Flood Simulations Using Fractional Flow and Scaled Mobilities
Authors: Al-Ibadi Hasan , Stephen Karl , Mackay Eric
Published: October 2021

We introduce a pseudoisation method to upscale polymer flooding in order to capture the flow behaviour of fine scale models. This method is also designed to improve the predictability of pressure profiles during this process. This method controls the numerical dispersion of coarse grid models so that we are able to reproduce the flow behaviour of the fine scale model.

To upscale polymer flooding, three levels of analysis are required such that we need to honour (a) the fractional flow solution, (b) the water and oil mobility and (c) appropriate upscaling of single phase flow. The outcome from this analysis is that a single pseudo relative permeability set that honours the modification that polymer applies to water viscosity modification without explicitly changing it. The shape of relative permeability can be chosen to honour the fractional flow solution of the fine scale using the analytical solution. This can result in a monotonic pseudo relative permeability set and we call it the Fractional-Flow method. To capture the pressure profile as well, individual relative permeability curves must be chosen appropriately for each phase to ensure the correct total mobility. For polymer flooding, changes to the water relative permeability included the changes to water viscosity implicitly thus avoiding the need for inclusion of a polymer solute. We call this type of upscaling as Fractional-Flow-Mobility control method.

Numerical solution of the upscaled models, obtained using this method, were validated against fine scale models for 1D homogenous model and as well as 3D models with randomly distributed permeability for various geological realisations. The recovery factor and water cut matched the fine scale model very well. The pressure profile was reasonably predictable using the Fractional-Flow-Mobility control method.

Both Fractional-Flow and Fractional-flow-Mobility control methods can be calculated in advance without running a fine scale model where the analysis is based on analytical solution even though produced a non-monotonic pseudo relative permeability curve. It simplified the polymer model so that it is much easier and faster to simulate. It offers the opportunity to quickly predict oil and water phase behaviour.
Deep Learning Assisted Doppler Sensing for Hydrocarbon Downhole Flow Velocity Estimation
Authors: Katterbauer Klemens , Marsala Alberto , Schoepf Virginie , Abbassi Linda
Published: October 2021

Logging hydrocarbon production potential of wells has been at the forefront of enhancing oil and gas exploration and maximize productivity from oil and gas reservoirs. A major challenge is accurate downhole fluid phases flow velocity measurements in production logging due to the criticality of mechanical spinner-based sensor devices. Ultrasonic Doppler based sensors are more robust and deployable either in wireline or logging while drilling (LWD) conditions; however, due to the different sensing physics, the measurement results may not be equivalent. We present in this work an innovative deep learning framework to estimate spinner phase velocities from Doppler based sensor velocities. Tests of the framework on a benchmark dataset displayed strong estimation results. This allows for the real-time automatic interpretative framework implementation and flow velocity estimations either in conventional wireline production logging technologies (PLTs) and potentially also in LWD conditions, when the well is flowing in underbalanced conditions.
Investigation on the Effect of Mud Additives on the Gelation Performance of PAM/PEI System for Lost Circulation Control
Authors: Shamlooh Mohamed , Hamza Ahmed , Hussein Ibnelwaleed A. , Nasser Mustafa S. , Salehi Saeed
Published: October 2021

Lost circulation is one of the most common problems in the drilling of oil and gas wells where mud escapes through natural or induced fractures. Lost circulation can have severe consequences from increasing the operational cost to compromising the stability of wells. Recently, polymeric formulations have been introduced for wellbore strengthening purposes where it can serve as Loss Circulation Materials (LCMs) simultaneously. Polymeric LCMs have the potential to be mixed with drilling fluids during the operation without stopping to avoid non-productive time. In this study, the significance of most common conventional mud additives and their impact on the gelation performance of Polyacrylamide (PAM) / Polyethyleneimine (PEI) has been investigated.

Drilling fluid with typical additives has been designed with a weight of 9.6 ppg. Additives including bentonite, barite, CarboxyMethylCellulose (CMC), lignite, caustic soda, desco, and calcium carbonate has been studied individually and combined. Each additive is mixed with the polymeric formulation (PAM 9% PEI 1%) with different ratios, then kept at 130°C for 24 hrs. Rheological performance of the mature gel has been tested using parallel plate geometry, Oscillatory tests have been used to assess the storage Modulus and loss modulus. Moreover, the gelation profile has been tested at 500 psi with a ramped temperature to mimic the reservoir conditions to obtain the gelation time. The gelation time of the polymer-based mud was controllable by the addition of a salt retarder (Ammonium Chloride), where a gelation time of more than 2 hours could be achieved at 130°C.

Laboratory observations revealed that bentonite and CMC have the most effect as they both assist in producing stronger gel. While bentonite acts as a strengthening material, CMC increases the crosslinking network. Bentonite has successfully increased the gel strength by 15% providing a storage modulus of up to 1150 Pa without affecting the gelation time.

This work helps in better understanding the process of using polymeric formulations in drilling activities. It provides insights to integrate gelling systems that are conventionally used for water shut-off during the drilling operation to replace the conventional loss circulation materials to provide a higher success rate.