bluuebunny/crossref_metadata_2025_split huggingface dataset:
Synthesis and Characterization of Magnetic Nanodroplets for Flowback Analysis in Fractured Reservoirs
Authors: Ahmadi Seyedeh Hannaneh , Ding Boxin , L. Bryant Steven , Kantzas Apostolos 
Published: March 2023

AbstractHydraulic fracturing has facilitated the rapid development of tight reservoirs throughout the world in the past decades. A common challenge arising from multistage hydraulic fracturing is the accurate characterization of the complexity and conductivity of the fracture networks, for optimizing the well performance as well as the subsequent production. Prevalent operations carried out on hydraulically fractured wells are tracer injections along with the fracturing fluid and flowback analysis, which are indispensable to condition the well for long-term performance and decrease the operational time. In this paper, a new polymer-coated iron oxide (Fe3O4) nanoparticle (NP) has been synthesized, which can emulsify and stabilize nano-oil-droplets in a continuous water phase and these Pickering nanodroplets provide potential applications for the characterization of fractures by flowback analysis in tight reservoirs due to their pivotal properties, including being superparamagnetic with ability to detect in-situ, easily synthesized, size controllable, strong stability, minimal retention in fractures and environmental benign features. To apply these magnetic nanodroplets for fracture characterization, two concerns should be considered, including the long-term stability and transport behavior of these Pickering nanodroplets, which is demonstrated in this study.Herein, iron oxide nanoparticles were firstly functionalized to improve their hydrophilicity, and then nanoemulsion samples were emulsified utilizing these engineered nanoparticles. Two different factors, including different hydrocarbons and emulsification energy, were considered to investigate their impact on the stability of the nanoemulsion. This is because they are extremely important for the stabilization of the Pickering nanoemulsion. As a result, some characterization tests were performed to recognize the stability behaviour of the systems and structure of nanoemulsion through nanodroplet size distribution, z-potential, bulk rheology, and screening tests. Moreover, the nanoemulsion stability is examined through low-field nuclear magnetic resonance (NMR) relaxometry and X-ray CT imaging. Experimental results reveal that carefully synthesized polymer-coated Fe3O4 NPs can emulsify the oil and water to form a sufficiently stable oil-in-water (O/W) Pickering nanoemulsion. The optimized composition to have a more stable emulsion is using hexadecane as the oil phase because of its high density and low solubility in water to reduce the Ostwald Ripening. An emulsification energy of 40 kJ is found to generate optimum droplet size distribution, thus providing the best nanoemulsion stability.
Automated Production Forecasting Using a Novel Machine Learning Based Approach
Authors: Shrivastava Kaustubh , Shukla Priyavrat , Banerjee Raj , Koley Manas 
Published: March 2023

AbstractProduction forecasting and hydrocarbon reserve estimation play a major role in production planning and field evaluation. Traditional methods of production forecasting use historical production data and do not account for completion and geolocation attributes that limit their prediction ability, especially for wells with a short production history. In this paper, we present a novel data-driven approach that accounts for the completion and geolocation parameters of a well along with its historical production data to forecast production.In this work, we used supervised learning to develop an ensemble of machine learning (ML) based models to forecast production behavior of oil and gas wells. The developed models account for historical production data, geolocation parameters, and completion parameters as features. The dataset used to create the models comprises publicly available data from 80,000 unconventional wells in North America. The developed models are rigorously tested against 5% of the original data set. The models are systematically studied and compared against traditional forecasting techniques and results are presented here.The created ensemble of models was tested by forecasting the production of 3,700 wells and the obtained results were compared against real production data. We show that the models clearly capture the natural decline trend of the produced hydrocarbon. In cases where the natural decline of the well has been temporarily modified, possibly due to operations, the production during other periods of the time series matches the prediction. This indicates that, unlike in traditional methods, such changes don't adversely impact the forecasting ability of our method.We also conducted a systematic investigation and compared the forecast from the developed model against the forecast from a traditional method (Arps, 1945). During the comparison, it was observed that for short-production history wells (available production data from 2 to 12 months), the error rate in the predicted production behavior from traditional methods was higher when compared with the developed method. As the quantity of historical production data increases, the forecasting ability of traditional methods improves. By comparison, the decline from the developed method matches the real production data for both short- and long-production history wells, and clearly outperforms the traditional methods based on blind tests.In this work, we present a novel ML based approach for forecasting production. This approach overcomes the challenge of the traditional time-series forecasting techniques that use only the past data for forecasting. It also incorporates static parameters (completion and geolocation parameters) in its architecture. The developed method leverages statistical averaging by employing an ensemble of random forest models, making the developed approach better than traditional ML based methods (ARIMA and LSTM) for forecasting time-series data.
Buildup Evaluation of an Overpressured Naturally Fractured Carbonate
Authors: Azuara Diliegros Brenda , Aguilera Roberto 
Published: March 2023

AbstractThis paper examines the buildup pressure response of a well that penetrates an unconventional tight naturally fractured carbonate reservoir in Mexico. Four buildups in the same well over a period of 4 months, with intermediate flow periods, suggest partial closure of natural fractures. Radial flow is dominant in the four buildups. This is recognized in semilogarithmic and pressure derivative cross plots. However, the formulations require a consistent empirical component to match the buildup data.The four buildup tests are evaluated with a semi-empirical dual-porosity model with restricted interporosity flow. The restricted flow between matrix and fractures is the result of partial secondary mineralization (cementation) within the fractures, which can be visualized as a natural positive skin that reduces the oil flow from the matrix to the fractures. The empirical part of the method is provided by a severity exponent (SE), which helps improving the match of the buildup semilog and derivative plots.The buildup evaluations permit estimating several parameters of interest, including fracture capacity (k2.h), skin, storativity ratio (ω), and the extrapolated pressure (p*). The calculated k2.h is equal to 8.73 md-ft during the first and second buildups, 8.34 md-ft during the third buildup and 5.5 md-ft during the last buildup, which is also the longest (118 hours). Results suggest that although natural fractures are present, they tend to close once the well goes on production. Thus, the conclusion is reached that the carbonate reservoir is tight and likely stress dependent. The values of skin tend to increase going from −2.51 to −2.20 to −1.45 to +0.46 indicating that the reservoir goes from improved condition around the wellbore to slightly damaged conditions probably due to fracture closure. On the other hand, ω increases continuously going from 0.46 to 0.56 to 0.66 to 0.71 suggesting a tendency of the reservoir to move from dual to single porosity behavior. The reservoir is overpressured (0.87 psi/ft) and the extrapolated pressures (p*) decrease from 18,200 to 18,050 to 16,700 to 14,000 psi because of the tight characteristics of the reservoir. However, given the large size of the reservoir, the likelihood of depletion is unlikely.The novelty of this study is the development of a new easy-to-use semi-empirical well testing model for matching the buildup pressure response of four tests performed in a well that penetrates an overpressured, unconventional, tight naturally fractured carbonate. The tests could not be matched with well-known commercial packages.
Integrating Geochemical Fingerprinting Technology with Reservoir Surveillance Data – A Montney Case Study to Optimize Pad Design in Multi-Bench Development
Authors: Liang Peter , Liu Yuchen , Unick Kate , Curzon Troy , Jin Muqing , Wu Jiang , Liu Faye 
Published: March 2023

AbstractAn integrated approach was taken to utilize geochemistry information extracted from cuttings and produced oil samples to monitor vertical drainage between stacked reservoirs as well as to optimize well density in multi-bench cube development in the Montney play.Oil extracts from cuttings were profiled using high-resolution multi-dimensional gas chromatography (GCXGC) identifying 2,000+ compounds. Key reservoir properties such as indicators of oil saturation and matrix permeability were calculated using oil signatures extracted from cuttings. Subsequently produced oil were profiled using the same GCXGC method. Drainage frac height (DFH) and quantitative vertical production allocation by zone was conducted by building a geochemistry-based model and correlating the produced oils back to their contributing intervals represented by the cuttings.In this Montney case study, two neighboring pads with different cube designs were investigated. Pad 1 is a 12-well pad with 4 target benches and a stacked wellbore placement pattern, whereas Pad 2 is a 6-well pad with 3 target benches and a staggered wellbore placement pattern. Cuttings and produced oil samples were collected from both pads at every development bench, and geochemical DFHs and production allocations were estimated. The geochemical index from cuttings were in-line with the porosity log and wetness balance log collected while drilling. Based on the geochemical production allocation, both Pad 1 and Pad 2 showed vertical fracture growth was not constrained within the target bench. As a result, more vertical overlaps were detected which leads to a more severe vertical communication problem observed on Pad 1 where wellbores were vertically stacked above or below each other.In this study, the geochemical fingerprinting result was used to calibrate microseismic events. The quantitative production allocation allowed us to filter out any non-effective events and realistically capture the productive stimulated rock volume for optimizing well spacing and landing depth. Last but not least, the geochemical production allocation was successfully integrated with other reservoir surveillance data such as interference well testing.
Diverting Fracturing Stimulation Technique Using a Novel Temporary Plugging Agent with Multiphase Transition Properties at Different Temperatures
Authors: Chen Xiang , Liu Pingli , Zhao Liqiang , Du Juan , Zheng Jiangang , Chen Zhangxing , Yang Jian , Zhao Wanwei , Liu Fei , Lou Fengcheng , Wang Guan , Liu Jinming , Huang Chengxi 
Published: March 2023

AbstractGiven the fact that diverting fracturing technique can improve the effective stimulation reservoir volume, and the currently-used temporary plugging materials of chemical particles and fibers are difficult to pass through sand-control completion tools and enter into fractures due to their solid nature, this work thus developed a novel temporary plugging agent (TPA) with multiphase transition properties at different temperatures. Laboratory and field experiments were both conducted to study its feasibility on industrial field applications.Laboratory experiments were first carried out to investigate the properties of this TPA, including multiphase transition temperature and time, plugging strength, compatibility with other fluids, and core permeability damage, in order to guide the design of plugging agent dosage, fracturing construction parameters, and wellbore-fracture temperature. Then, field experiments were conducted to demonstrate its feasibility on actual field applications. Well A and Well R with almost the same geological and engineering conditions were chosen in this experiment where Well A adopted the developed novel technique and Well R, as a comparison well, adopted a conventional fracturing technique.The results from the laboratory experiments indicated that the performance of this TPA met the requirements of industrial standards. With an increase in temperature, this TPA underwent a solution (liquid state) - gel (semi-solid state) - solution (liquid state) transition to meet the needs of different stages in a fracturing treatment, and its multiphase transition speed was controllable. Its plugging strength was positively correlated with its plugging length, with a gradient of 8.9MPa/m. This TPA had good compatibility with other fluids and little damage to rock permeability, only 2%, much less than 25% specified in the standard.The results from the field experiments demonstrated that this innovative technique was feasible and effective. The construction curve of Well A indicated that the construction pressure increased by 3.1MPa and the formation broke again after injecting this TPA. The micro-seismic monitoring also supported this finding and showed that new fractures propagated to the north-by-east direction instead of the due west direction. Under the same production system, the initial daily gas production of Well A was 1.3 times that of Well R. After 100 days of production, the daily gas production of Well A was 1.5 times that of Well R.
Effective Thermal Conductivity of Tight Porous Media
Authors: Ghasemi Shahab , Chourio Arocha Geragg , Fayazi Amir , Kantzas Apostolos 
Published: March 2023

AbstractFluid and heat flow in complex porous media is widely used in various sciences such as medicine, environmental engineering, geoscience, and petroleum engineering. Understanding flow and heat transfer is may be difficult unless the pore geometry is well understood. The focus of this study is the determination of effective thermal dispersivity by both conduction and forced convection. For this purpose, experimental measurements and simulation results of heat and momentum transfer are presented. Experiments are conducted in a sand pack with various surrounding temperatures and injection rates. A 3-D heat transfer model was developed with and without fluid flow with three components. First component is mobile or stagnant fluid in the pore space, second component is the sandstone continuous matrix, and the third component is another solid that has a separate thermal conductivity and will mimic the constant temperature boundary. The transfer of the heat through the solid and fluid and also from the solid to the fluid is related to the composition and connectivity of the solid in the geometry. However, when there is forced convection, the key factor is the Peclet number. The velocity of the fluid can change the effective thermal conductivity up to four orders of magnitude. For the experiments, a sand pack 48cm long was used at temperatures of 40 - 60 °C and water injection rates of 1 - 100 cc/min. The model is augmented by numerical calculations of heat transfer parameters such as effective thermal conductivity and effective thermal advection by monitoring the Peclet number of the process. The variability of thermal dispersion of tight systems under specific composition and pore topology was presented.
Using Thermoelectric Generators (TEGs) for Electric Power Generation from Waste Heat in Geothermal Plants
Authors: Nourdanesh Nader , Kantzas Apostolos 
Published: March 2023

AbstractThe application of Thermoelectric Generators for converting geothermal energy to electricity is investigated in this paper, considering the effects of various parameters on their efficiency. Using renewable energy is one of the latest solutions to tackle climate change, global warming, air pollution, the ozone layer hole, etc. In this regard, a thermoelectric generator (TEG), which is a device for converting heat energy to power, could be used in different renewable energy systems like solar or geothermal. Although their initial cost is lower than the other methods of converting thermal energy to electricity, their efficiency is lower than the most common techniques. Therefore, finding the optimum situation to increase the efficiency of combined devices with TEGs can increase the likelihood of using them in different industries. The parameters investigated in this paper were the temperature of the operating fluid in channels, the optimum mass flow rate, and the channel slope. The results have shown that with increasing temperature differences of the fluid in the channel, the efficiency of the designed system increased significantly. In addition, by increasing the mass flow rate of the operating fluid in the channel, power generation and the whole system efficiency increase up to a certain plateau value. Furthermore, the experimental results have proven that increasing the slope of the channel increased the amount of power generation but has not caused a noticeable change in their efficiency.
Dynamic Surrogate Model for Oil Production Rates Prediction in SAGD Processes
Authors: Guevara Jose L. , Trivedi Japan 
Published: March 2023

AbstractIn this paper we propose a novel framework for the identification of a dynamic surrogate model (DSM) that can offer a fast and effective prediction of time-varying outputs (e.g., oil rates) of a Steam Assisted Gravity Drainage process. In the framework, the prediction at any given time consists of the addition of two components: a base model plus a correction term. The former is represented by a conventional one-step forecast nonlinear model(s) used recursively to make n-steps ahead forecast. The latter is modeled error term, rationalized under the assumption that the forecast error given by the base model is correlated with time due to the recursive strategy used. Since at every time step the input to the one-step forecast model is the prediction made by the same model in the previous time step, so an error accumulation is expected as the prediction time increases. This is analogous to the well-known geostatistical Kriging method, in which the basic assumption is that prediction errors are not constant, rather they are correlated with distance, and as a result, they can be modeled separately using covariance models. The identification of the base and correction model follows the typical surrogate model framework, i.e., design of experiments, evaluation of the samples, and construction of the models. In the context of DSM, the design of experiments represents the random selection of a set of steam injection policies in the preestablished production horizon. For each of these samples, a corresponding oil production rate time series is obtained using a reservoir simulation model; this model was built using publicly available data from Norther Alberta SAGD implementations. Afterwards, the base model and correction term are identified using Long-Short Term Memory neural networks. Results show that DSM significantly outperforms the conventional one-step forecast nonlinear models used recursively. In particular DSM offers, a significant increase of median R2 value of over 0.88 and a reduction of the median and standard deviation of Mean Absolute Percentage Error of over 67.0% and 80.1%, respectively. These results suggest that DSM is able to offer effective (low error and high R2) and efficient (relatively low number of samples) to identifying computationally inexpensive surrogate models for the prediction of time-varying outputs. Furthermore, the framework holds promise to be useful in SAGD optimization efforts, such as, finding the optimal steam injection policy.
Optimal Degree of Upgrading from Solvent Processes
Authors: Azom Prince , Ben-Zvi Amos 
Published: March 2023

AbstractInjection or co-injection of solvents has been proposed as an in-situ recovery mechanism for bitumen (Gupta et al., 2002). Solvent-based recovery schemes have several potential advantages over steam-based schemes such as SAGD. These benefits include lower GHG emissions and water requirements, lower capital intensity, and lower operating costs. Additionally, solvent-based recovery schemes have been proposed to improve the quality of produced bitumen (Jossy et al., 2008).The primary mechanism for in-situ upgrading during a solvent injection process is solvent de-asphalting. Solvent de-asphalting is a proven commercial process for processing and upgrading oil in surface facilities such as refineries and upgraders. Several laboratory studies have shown that solvent de-asphalting can also occur in situ, resulting in API improvement in the produced bitumen (AITF report, 2017 and 2018, Brons and Yu, 1995).In this work the economic benefits of upgrading bitumen in-situ will be studied. It will be shown that, at a given price environment, there exists an optimum level of upgrading (as measured by °API). Generally, too little upgrading reduces the value of bitumen due to blending and transportation costs, while too much upgrading reduces bitumen yield and ultimate recovery.
Dielectric Measurement Method for Dynamic Monitoring of Water-Saturated Sand Pack with and without Evaporation
Authors: Arabloo Milad , Kuznetcov Ilia , Fayazi Amir , Kantzas Apostolos 
Published: March 2023

AbstractThermal-based recovery techniques will continue to be used in bitumen and heavy oil recovery. Among them, electromagnetic (EM) heating is promising, especially in situations in which there are technical and environmental issues with steam-based methods. To design the EM process, precise knowledge of the dielectric properties of the target formation is of great importance. To achieve the research goals, a new patented impedance spectroscopy setup is used. An alternating electric field is applied to the sample using an impedance analyzer. The impedance measurements of the clean sand sample, which is pre-saturated with deionized water or brine, are measured at an elevated temperature and pressure to mimic reservoir conditions. There are two successive cycles of heating up to 170 °C. The first heating cycle is followed by a cool-off period, while the second heating cycle culminates with evaporation. The impedance was dynamically measured during each heating cycle. In addition, the dielectric properties of fresh water-gas and brine saturated sands were demonstrated. Results show that the electrical conductivity and dielectric constant of the samples are significantly influenced when the NaCl solution is the saturating fluid. Downward trends in both the electrical conductivity and relative dielectric constant were observed as the steam quality improved, which indicates the creation of a desiccated zone.