bluuebunny/crossref_metadata_2025 huggingface dataset:
Privacy-Focused Blockchain Solution for Production Royalty Management
Authors: Lakhanpal Vikrant , Samuel Robello
Published: April 2021

Oil and gas operators have a multitude of tasks to perform for smooth business operations. In addition to various engineering, technical, and operational decisions, there are back-office operations that should be efficient to keep business running smoothly, including production royalty management (PRM). The current system for PRM is extremely complex and costly, and involvesmultiple parties/contracts and often causes disputes.

A blockchain-based solution allows the organizations to better navigate complex laws, courtrulings, and legal jargon that determine how billions of dollars are distributed every year. There are two aspects to PRM —royalties owed and royalties received—and the proposed solution caters to both operators and independent mineral owners. The operator should compile production and revenue data monthly and distribute it to all owners as per their individual lease. The expenses and marketing costs should be entered into the accounting system before revenue is distributed. Owners may be skeptical about data shared; therefore, the entire process can take up to two or more months. Furthermore, privacy is of utmost importance because theoperator can neither share data between owners nor disclose proprietary information.

This paper proposes the entire royalty management system be hosted on a permissioned blockchain. Given their intrinsic nature of trustworthiness and transparent execution of transactions involving multiple parties, blockchain can streamline the royalty distribution process. The design and implementation of a smart contract for consent-driven and double-blind data sharingon a blockchain platform is presented. The system is designed for both inter- and intra-organizational transaction—a local peer interacts with an associated anchor peer within the globalnetwork. In addition to transparency and efficiency, this method provides important features ofprivacy and auditability while limiting ledger size. Further, peer identities are hiddenfrom each other while they collectively agree on transaction outcomes, and data will never be shared among the peers of the blockchain without explicit consent from the owners to ensure that the conflicting objectives of privacy and transparency are met.

Blockchain is peer-to-peer decentralized digital ledger technology with promising potential. Even after all the recent innovations, the owner-relations department still operates using old methods. An innovative blockchain-based solution to reduce time and money spent on disbursing revenue for the operator and to increase the time value of information and trust in the system for the owner is presented.
Advanced Machine Learning Methods for Prediction of Fracture Closure Pressure
Authors: Ibrahim Mohamed Mohamed , Mehta Dinesh , Ozkan Erdal
Published: April 2021

Determining the closure pressure is crucial for optimal hydraulic fracturing design and successful execution of fracturing treatment. Historically, the use of diagnostic tests before the main fracturing treatment has significantly advanced to gain more information about the pattern of fracture propagation and fluid performance to optimize the designs. The goal is to inject a small volume of fracturing fluid to breakdown the formation and create small fracture geometry, then once pumping is stopped the pressure decline is analyzed to observe the fracture closure.

Many analytical methods such as G-Function, square root of time, etc. have been developed to determine the fracture closure pressure. There are cases in which there is difficulty in determining the fracture closure pressure, as well as personal bias and field experiences make it challenging to interpret the changes in the pressure derivative slope and identify fracture closure. These conditions include: High permeability reservoirs where fracture closure occurs very fast due to the quick fluid leakoff.Extremely low permeability reservoir, which requires a long shut-in time for the fluid to leak off and determine the fracture closure pressure.The non-ideal fluid leak-off behavior under complex conditions.

The objective of this study is to apply machine learning methods to implement a predesigned algorithm to execute the required tasks and predict the fracture closure pressure while minimizing the shortcomings in determining the closure pressure for non-ideal or subjective conditions.

This paper demonstrates training different supervised machine learning algorithms to help predict fracture closure pressure. The workflow involves using the datasets to train and optimize the models, which subsequently are used to predict the closure pressure of testing data. The output results are then compared with actual results from more than 120 DFIT data points. We further propose an integrated approach to feature selection and dataset processing and study the effects of data processing on the success of the model prediction.

The results from this study limit the subjectivity and the need for the experience of personal interpreting the data. We speculate that a linear regression and MLP neural network algorithms can yield high scores in the prediction of fracture closure pressure.
Chemical EOR in Low Permeability Sandstone Reservoirs: Impact of Clay Content on the Transport of Polymer and Surfactant
Authors: Guetni Imane , Marlière Claire , Rousseau David
Published: April 2021

Application of chemical enhanced oil recovery (C-EOR) processes to low-permeability sandstone reservoirs (in the 10-100 mD range) can be very challenging as strong retention and difficult in-depth propagation of polymer and surfactant can occur. Transport properties of C-EOR chemicals are particularly related to porous media mineralogy (clay content). The present experimental study aimed at identifying base mechanisms and providing general recommendations to design economically viable C-EOR injection strategies in low permeability clayey reservoirs.

Polymer and surfactant injection corefloods were conducted using granular packs (quartz and clay mixtures) with similar petrophysical characteristics (permeability 70-130 mD) but having various mineralogical compositions (pure quartz sand, sand with 8 wt-% kaolinite and sand with 8 wt-% smectite). The granular packs were carefully characterized in terms of structure (SEM) and specific surface area (BET). The main observables from the coreflood tests were the resistance and residual resistance factors generated during the chemical injections, the irreversible polymer retention and the surfactant retention in various injection scenarios (polymer alone, surfactant alone, polymer and surfactant).

A first, the impact of the clay contents on the retention of polymer and surfactant considered independently was examined. Coreflood results have shown that retention per unit mass of rock strongly increased in presence of both kaolinite and smectite, but not in the same way for both chemicals. For polymer, retention was about twice higher with kaolinite than with smectite, despite the fact that the measured specific surface area of the kaolinite was about 5 times less than that of the smectite. Conversely, for surfactant, retention was much higher with smectite than with kaolinite.

Secondly, the impact of the presence of surfactant on the polymer in-depth propagation and retention was investigated in pure quartz and kaolinite-bearing porous media. In both mineralogies, the resistance factor quickly stabilized when polymer was injected alone whereas injection of larger solution volumes was required to reach stabilization when surfactant was present. In pure quartz, polymer retention was shown, surprisingly, to be one order of magnitude higher in presence of surfactant whereas with kaolinite, surfactant did not impact polymer retention.

The results can be interpreted by considering adsorption-governed retention. The mechanistic pictures being that (a) large polymer macromolecules are not able to penetrate the porosity of smectite aggregates, whereas surfactant molecules can, and (b) that surfactant and polymer mixed adsorbed layers can be formed on surfaces with limited affinity for polymer. Overall, this study shows that C-EOR can be applied in low permeability reservoirs but that successful injection strategies will strongly depend on mineralogy.
Numerical Evaluation of the Impact of Steam Quality on Steam Flood Performance in a Kuwait Heavy Oil Reservoir
Authors: Al-Ali Yacob , Al-Rubah Abdullah , Verlaan Marco
Published: April 2021

The objective of this study is to assess any opportunities to improve field recovery or thermal efficiency by evaluating different steam quality scenarios and their impact on the performance of the cyclic steam stimulation and steam flood in Lower Fars reservoir.

In this study, simulation history matching of the dynamic test data from the ongoing thermal pilots was used to validate the static and dynamic description. The process results in an improved dynamic model to be used specifically for the steam quality scenarios evaluation, which was then used in the prediction mode for deciding on an optimum steam quality percentage for the upcoming steam flood operation.

Different bottom-hole steam quality scenarios are defined using different steam quality output values at the steam generator and a fixed amount of surface network heat loss. The wellbore heat losses are explicitly modelled to arrive at bottom hole steam quality corresponding to a boiler steam quality.

The impact of the steam quality on the cumulative amount of oil produced is significant when an economic steam oil ratio cutoff is applied. There was an overall 40% difference in cumulative oil production between low and high steam quality cases, and a 30% difference when an energy cut off criterion was applied instead of the steam oil ratio cutoff. The highest steam quality resulted in the best performance in terms of oil recovery and energy efficiency. Analysis of the results show that the effect of steam quality is different during different periods of the CSS/SF process and mainly related to the different amount of enthalpy injected. During the CSS period a lower steam quality results in lower oil recovery but at a better efficiency compared to a high steam quality. In the steam flood phase the high steam quality results in both higher recovery and higher energy efficiency. The latter is caused by lower over and underburden heat loss.

The bottom hole steam quality is a measure of the energy content of the steam that is delivered to the reservoir. This has a significant impact on the efficiency of the thermal recovery process. The steam quality can vary as function of well location and time for numerous reasons. Thus, it is essential to understand how these variables affect the recovery process.
Errors Associated With the Application of the G-Function Plot for Analyzing Pressure Fall-Off Tests in Water Injection Wells
Authors: Wu Jun , Ershaghi Iraj
Published: April 2021

Castillo1 suggested the use of the G-Function plot based on the work of Nolte2. It has been a standard practice in the fracturing community to estimate the fracture closing pressure from a tangent to the G*dp/dg plot. In this analysis technique, the assumption is that a fracture has already developed under the high-pressure fracturing fluid. Then when the pumping is relaxed, one can estimate the fracture closing pressure. In many California waterfloods, the issue of maximum allowable injection gradient has been debated. Various solutions have been proposed to calculate a safe injection gradient. One method that has been promoted is the application of the G-function plot. In this paper, we maintain that this application can be misleading using the prescribed cartesian G function plots.

We present the results of an extensive research study for analyzing pressure fall-off data using the G-Plot function. We studied a reappraisal of the G function plot using waterflood conditions where no prior fractures had formed, and no fracture closing pressure was meaningful or applicable. We show from analysis of generated data, using both numerical reservoir modeling and analytical derivations for a radial flow system, that fall-off tests analyzed using the cartesian G function can generate false indications of fracture closing where in fact, the entire injection has been based on radial flow homogeneous injection systems. We also studied systems with a pre-existing fracture before injection. We show that if such a reservoir system is subjected to injection and fall-off tests, again, one may compute a false indication of the irrelevant fracture closure pressure. We discuss how the cartesian scale used for the G function plot can be misleading for the analysis of fall-off test data.
Numerical Investigation of Proppant Transport and Placement Along Opened Bedding Interfaces
Authors: Xie Jun , Tang Jizhou , Sun Sijie , Li Yuwei , Song Yi , Huang Haoyong , Pei Hao , Zhang Fengshou
Published: April 2021

Slurry, as a proppant-laden fluid for hydraulic fracturing, is pumped into initial perforated cracks to generate a conductive pathway for hydrocarbon movement. Recently, numerous studies have been done to investigate mechanisms of proppant transport within vertical fractures. However, the distribution of proppant during stimulation becomes much more complicated if bedding planes (BPs), natural fractures (NFs) or other discontinuities pervasively distributed throughout the formation. Thus, how to capture the transport and placement mechanisms of proppant particles in the opened BPs becomes a significant issue. In this paper, we propose a closed-form continuous proppant transport model based on the conservation of total proppant volume and sedimentation of proppant particles. This model enables to integrate with the fluid flow section of a 3-D hydro-mechanical coupled fracture propagation model and then predict the distribution of proppant velocity and slurry volume fraction within a dynamic fracture network. Stokes’ law is applied to determine the sedimentation velocity. In the fracture propagation model, rock deformation is governed by the analytical solution of penny-shaped crack to determine fracture width. Fluid flow is characterized by finite differentiation scheme and then the fluid velocity is obtained. These two parameters above are inputs for the proppant transport model and both slurry viscosity and density are updated in this step. Afterwards, both fracture width and fluid velocity would be altered in the fracture model.

Analysis of the proppant distribution within crossing-shaped fracture is conducted to study mechanisms of proppant transport along opened BPs. From our numerical analysis, we find that the distribution of proppant concentration is independent with the fluid viscosity, but highly dependent on the volume fraction of pumping slurry, under a given pumping pressure. Due to the difference of viscosity and proppant volume fraction at locations of upper and lower BPs, we observe that two symmetric BPs are unevenly opened, with different channel length along BP. Moreover, the width of opened upper BP is much smaller than that of opened lower BP as a result of discrepancy of proppant sedimentation. Last but not the least, a criterion of flow bed mobilization is established for dynamically tracking the sedimentation along the BP. Then the effect of different parameters (such as proppant size, proppant density, fluid viscosity, injection rate) on proppant distribution along opened BPs is also studied.

Our model fully considers the proppant transport and settlement, proppant bed formation and interaction between fracture and proppant, which helps to predict the influence of proppant during fracturing treatment. Additionally, our model is also capable of dynamically tracking the settlement of proppant along opened BPs.
An Industry Overview of Downhole Monitoring Using Distributed Temperature Sensing: Fundamentals and Two Decades Deployment in Oil and Gas Industries
Authors: Soroush Mohammad , Mohammadtabar Mohammad , Roostaei Morteza , Hosseini Seyed Abolhassan , Mahmoudi Mahdi , Keough Daniel , Cheng Li , Moez Kambiz , Fattahpour Vahidoddin
Published: April 2021

Distributed Temperature Sensing (DTS) system using optical fiber has been deployed for downhole monitoring over two-decades. Several technological advancements led to a wide acceptance of this technology as a reliable surveillance technique. This paper presents a comprehensive technical review of all the applications of the DTS, with focus on oil and gas industrial deployments.

The paper starts with the advantages of the DTS over other methods and an overview of the DTS basics, including theory, the DTS components, deployment types, fiber types, design and limitations. Then, it is followed by the oil and gas applications of the DTS including hydraulic fracturing (during and after fracturing), well treatment/stimulation (acid injection, fluid distribution, diversion monitoring), inorganic (scaling) and organic (wax/asphaltene/hydrate) deposition detection, leak detection (in well and pipeline), flow monitoring (rate monitoring, water/steam injection and SAGD monitoring, CO2 storage monitoring, zonal contribution determination, gas lift optimization) and reservoir/fluid characterization (facies, porosity, permeability and fluid composition determination).

This study reviews the historical development, applications and limitations of the DTS systems. The paper mainly focusses on deployment techniques, the application of the DTS for the prediction and surveillance of the non-thermal and thermal producer/injector wells, hydraulically fractured wells and those wells with treatments. The paper provides a concise review using several field cases from over two hundred published papers of Society of Petroleum Engineering (SPE) and journal databases.

The application of the DTS in downhole monitoring can be divided into the qualitative and quantitative applications. In quantitative approaches, numerical models should be combined with the DTS data. This study discusses case by case worldwide field applications of DTS along with proposed modeling methods and interpretations. It also summarizes main challenges, including the fiber reliability, longevity, and operational limitations such as the installation and the complexity of quantitative approaches.

This study is the foundation for an ongoing study on wellbore and reservoir surveillance through real-time distributed fiber optic sensing recordings along the wellbore. It summarizes the historical development and limitations to identify the existing gaps and reviews the lessons learned through the two decades of the application of the DTS in production performance.
Parameters Estimation for Chemical Flooding Modeling Using Ensemble-Based Method
Authors: Wang Xindan , Zhang Yin , Dandekar Abhijit , Wang Yudou
Published: April 2021

Chemical flooding has been widely used to enhance oil recovery after conventional waterflooding. However, it is always a challenge to model chemical flooding accurately since many of the model parameters of the chemical flooding cannot be measured accurately in the lab and even some parameters cannot be obtained from the lab. Recently, the ensemble-based assisted history matching techniques have been proven to be efficient and effective in simultaneously estimating multiple model parameters. Therefore, this study validates the effectiveness of the ensemble-based method in estimating model parameters for chemical flooding simulation, and the half-iteration EnKF (HIEnKF) method has been employed to conduct the assisted history matching. In this work, five surfactantpolymer (SP) coreflooding experiments have been first conducted, and the corresponding core scale simulation models have been built to simulate the coreflooding experiments. Then the HIEnKF method has been applied to calibrate the core scale simulation models by assimilating the observed data including cumulative oil production and pressure drop from the corresponding coreflooding experiments. The HIEnKF method has been successively applied to simultaneously estimate multiple model parameters, including porosity and permeability fields, relative permeabilities, polymer viscosity curve, polymer adsorption curve, surfactant interfacial tension (IFT) curve and miscibility function curve, for the SP flooding simulation model. There exists a good agreement between the updated simulation results and observation data, indicating that the updated model parameters are appropriate to characterize the properties of the corresponding porous media and the fluid flow properties in it. At the same time, the effectiveness of the ensemble-based assisted history matching method in chemical enhanced oil recovery (EOR) simulation has been validated. Based on the validated simulation model, numerical simulation tests have been conducted to investigate the influence of injection schemes and operating parameters of SP flooding on the ultimate oil recovery performance. It has been found that the polymer concentration, surfactant concentration and slug size of SP flooding have a significant impact on oil recovery, and these parameters need to be optimized to achieve the maximum economic benefit.
A Systematic Design Approach for Bulk Gel Treatments Based on Average Gel Volume-Concentration Ratio
Authors: Aldhaheri Munqith , Wei Mingzhen , Bai Baojun
Published: April 2021

Controlling excessive water production in mature oil fields has always been one major objective of the oil and gas industry. This objective calls for planning of more effective water-control treatments with optimized designs to obtain more attractive outcomes. Unfortunately, planning such treatments still represents a dilemma for conformance experts due to the lack of systematic design tools in the industry.

This paper proposes and makes available a new design approach for bulk gel treatments by grouping designs of 62 worldwide field projects (1985-2018) according to gel volume-concentration ratio (VCR). After compiling them from SPE papers, the average gel volumes and polymer concentrations in the field projects were used to evaluate the gel VCR. Distributions of field projects were examined according to the gel VCR and the formation type using stacked histograms. A comprehensive investigation was performed to indicate the grouping criterion and design types of gel treatments. Based on mean-per-group strategy, the average VCR was estimated for each channeling and formation type to build a three-parameter design approach. Two approximations for the average polymer concentration and two correlations for minimum and maximum designs and were identified and included in the approach.

The study shows that the gel VCR is a superior design criterion for in-situ bulk gel treatments. Field applications tend to aggregate in three project groups of clear separating VCR cut-offs (<1, 1-3, >3 bbl/ppm). The channeling type is the dividing or distributing criterion of the gel projects among the three project groups. We identified that VCRs<1 bbl/ppm are used to treat conformance problems that exhibit pipe-like channeling usually presented in unconsolidated and fractured formations with very long injection time (design type I). For fracture-channeling problems frequently presented in naturally or hydraulically-fractured formations, VCRs of 1-3 bbl/ppm are used (design type II). Large gel treatments with VCR>3 bbl/ppm are performed to address matrix-channeling often shown in matrix-rock formations and fracture networks (design type III). Results show that the VCR approach reasonably predicts the gel volume and the polymer concentration in training (R2 of 0.93 and 0.67) and validation (AAPE <22%) samples. Besides its novelty, the new approach is systematic, practical, and accurate, and will facilitate the optimization of the gel treatments to improve their performances and success rate.
The First Application of a Novel Reservoir Simulation Technology Comprising Radial Drilling and Hydraulic Fracturing in the Niobrara Shale
Authors: Potapenko Dmitriy Ivanovich , Hart Timothy Brian , Kibsgaard Paal , Waters George Alan , Lewis Richard E. , Utter Robert J. , Brown J. Ernest , Ayala Salvador , Goudy Guy Thomas , Jelsma Henk H
Published: April 2021

A novel reservoir stimulation methodology combines oriented extended perforation tunnels of lengths up to 300 ft with specially designed hydraulic fracturing operations. The new stimulation methodology was introduced in the Florence Field in Colorado and primarily targeted the Niobrara Formation.

The technology test included two vertical wells completed with two and five pairs, respectively, of extended perforation tunnels, each 300 ft in length, jetted using radial drilling technology. The jetting operation was followed by a fracture stimulation treatment. The orientation of the extended perforation tunnels and the design of the fracturing treatment were based on analysis of the geological data and were defined using geomechanical modeling as well as far-field and near-wellbore flow simulations. The use of radial drilling technology to create extended perforation tunnels for the vertical wells offered a cost-effective way to significantly increase the reservoir contact area ofthe wellbore, making it similar to that of horizontal wells in the area. The engineered fracture treatments were performed at low treating pressures, and low proppant and fluid volumes.

The initial production rates of the two vertical wells included in this technology test exceeded expectations and are comparable to the initial production rate of the offset horizontal well that was completed in the same zone with significantly greatervolumes of proppant and fluid. The initial evaluation of the economic efficiency of this novel reservoir stimulation technology showed that its deploymentdelivered improved production rate to cost ratio for the second vertical well, compared to the reference horizontal well.

Based on the test results from the two wells, we conclude that the proposed reservoir stimulation methodology leads to substantial improvements in well production performance compared to traditional reservoir stimulation methods. Both the applied cost-effective approach for increasing the reservoir contact and the significantly lower resource intensity required for the hydraulic fracturing treatment further improve the economic benefits of this methodology.

This novel reservoir stimulation methodology opens the way for reconsidering well completion practices in the Niobrara Formation and holds significant potential for improving the hydrocarbon production economics in the Florence Field.
Lost Hills Solar Project: Powering an Oil and Gas Field with California Sunshine
Authors: Demayo Trevor N. , Herbert Nevil K. , Hernandez Dulce M. , Hendricks Jana J. , Velasquez Beberly , Cappello David , Creelman Ian
Published: April 2021

This paper outlines one of the first efforts by a major oil and gas company to build a net exporting, behind the meter solar photovoltaic (PV) plant to lower the operating costs and carbon intensity of a large, mature oil and gas field in Lost Hills, California. The 29 MWAC (35 MWDC) Lost Hills solar plant, commissioned in April 2020, covers approximately 220 acres on land adjacent to the oil field and is designed to provide more than 1.4 billion kilowatt hours of solar energy over 20 years to the field's oil and gas field production and processing facilities. The upgrades to the electrical infrastructure in the field also include new technology to reduce the risk of sulfur hexafluoride (SF6) emissions, another potent greenhouse gas (GHG).

Prior to solar, the Lost Hills field was importing all its electricity from the grid. With the introduction of the Innovative Crude Program as part of California's Low Carbon Fuel Standard (LCFS) and the revisions to the California Public Utilities Commission Net Energy Metering program, Lost Hills was presented with a unique opportunity to reduce its imported electricity expenses, reduce its carbon intensity, while also generating LCFS credits. The plant was designed to power the field during the day and export excess power to the grid to help offset night-time electricity purchases. The solar plant operates under a Power Purchase Agreement (PPA) with the solar PV provider and, initially, will meet approximately 80% of the oil field's energy needs. Future plans include the incorporation of lithium ion batteries, DC-coupled with the solar inverters, and with a total capacity of 20 MWh. This energy storage system will increase the amount of solar electricity fed directly into the field and reduce costs by controlling when the site uses stored solar electricity rather than electricity from the grid. The battery system will also increase the number of LCFS credits by 15% over credits generated by solar alone. Together, solar power plus energy storage provides a robust renewable energy solution.

This project will generate multiple co-benefits for the Lost Hills oil field by lowering the cost of power, reducing GHG emissions, generating state LCFS credits and federal Renewable Energy Certificates, and demonstrating a commitment to energy transition by investing in renewable technology. Hopefully, Lost Hills solar can be a model for similar future projects in other oil fields, not only in California, but across the globe.
Modeling of Injection-Induced Particle Movement Leading to the Development of Thief Zones
Authors: Qi Qianru , Ershaghi Iraj
Published: April 2021

This paper is a contribution to failure prediction of unconsolidated intervals that could have a negative impact on injection efficiency because of susceptibility to structural changes under fluid injection processes. In unconsolidated formations, formation fines may be subjected to drag forces by injected water because of poor cementation. This results in small grain moments, and continuation can result in a gradual increase in permeability and eventual development of washed-out or thief zones. This paper presents a new modeling approach using information from profile surveys and grain and pore size distribution to model the process of injection and the induced particle movement.

The motivation came from field observations and realization of permeability increase from profile surveys and substantial fines movement, leading to an increase in rock permeability. A series of case studies based on realistic published data on pore and grain size distribution are included to demonstrate the estimated increases in formation permeability. In our modeling approach, once we establish the range of grain sizes that fits the criterion for particle movement, a probabilistic algorithm, developed for the study, is applied to track changes in porosity and associated variations in permeability. This algorithm, presented for the first time, considers a stochastic approach to monitor the reservoir particle movements, pore size exclusion by particle accumulation and their resultant changes in rock properties. For this methodology, we ignored potential effects of wettability and clay swelling, and considered perfect spheres to represent the various grain sizes.

Predictions made using various realizations of channel formation and petrophysical alterations show the significance of having access to three sources of information; pore size distribution, grain size distribution, and profile surveys. Through inverse modeling using these pieces of information for a particular formation, we demonstrate how we can predict realistic changes and map rock transport properties.
Modeling and Simulation of Asphaltene Inhibition and Remediation Using Aromatic Solvents
Authors: Abouie Ali , Sepehrnoori Kamy
Published: April 2021

Asphaltene precipitation and deposition occur in the reservoir, near-wellbore, inside the tubing, and production facilities during primary, secondary, or tertiary production. As more water-flooded oil fields produce under miscible gas flooding, this problem becomes more common around the world. If asphaltene deposition occurs in the reservoir or wellbore, it can severely affect the economics of the field in terms of production loss, intervention cost, and the requirement for chemical additives, if necessary. In some severe cases, intervention would be impossible and side-track well needs to be drilled. Hence, the best strategy for oil production in asphaltenic reservoirs is to control asphaltene precipitation and deposition through prevention and remediation jobs to minimize the number of well shut-ins, the downtime of the wells, and the associated cost.

In this paper, we reviewed the common asphaltene prevention and remediation techniques along with their pros and cons. Since removing asphaltene deposits from the problematic wells is relatively expensive and sometimes requires substantial downtime of the well, we focused on one of the prevention techniques (i.e., continuous solvent injection through capillary injection string), which has become more popular, to control asphaltene precipitation in the wellbore. We obtained the physical properties of an aromatic solvent from literature and then characterized it as a component to be used with PC-SAFT EOS. Subsequently, we used the in-house wellbore model to evaluate the effectiveness of the continuous solvent injection with different injection rates on preventing asphaltene precipitation and deposition along the wellbore.
Experimental Investigation of Hybrid Techniques for Enhancing Alaska North Slope Viscous Oil Recovery
Authors: Cheng Yaoze , Zhang Yin , Dandekar Abhijit
Published: April 2021

Alaska North Slope (ANS) contains vast viscous oil resources that have not been developed effectively due to the lack of efficient Enhanced Oil Recovery (EOR) techniques. This study investigates the EOR performance of three new hybrid EOR techniques: HSW-LSW-LSP flooding, solvent-alternating-LSW flooding, and solvent-alternating-LSP flooding. Additionally, pressure-volume-temperature (PVT) tests have been conducted to investigate the oil swelling and viscosity reduction effects of the proposed solvent. It has been found that the oil recovery of HSW flooding was 43.42%, while the tested HSW-LSW-LSP flooding, solvent-alternating-LSW flooding, and solvent-alternating-LSP flooding could improve the oil recovery to 74.17%, 79.73%, and 85.87%, respectively. All three proposed hybrid EOR techniques can significantly enhance the viscous oil recovery since they were all designed to both improve the microscopic and macroscopic sweep efficiencies. In particular, the PVT test results confirmed that the proposed solvent in this study could effectively swell the viscous oil over 30% and significantly reduce the viscous oil viscosity by about 97%. However, the initially designed solvent-alternating-LSP flooding displacement experiment had to be terminated after the first slug of polymer injection due to the extremely high differential pressure, which was over the measurement range of the pressure gauge. Thus, the actually tested displacement process in this study was solvent-LSP flooding. The extremely high differential pressure was found to result from the polymer deposit and blockage at the outlet. Although the proposed solvent-alternating-LSP flooding may produce the highest oil recovery, it cannot be applied in the field until the issue of polymer precipitation is understood and solved. The study has practical guidance to the selection of proper EOR techniques to enhance viscous oil recovery on ANS.
Predicting Heavy Oil Combustion Kinetics with Machine Learning
Authors: Anderson Timothy I. , Li Yunan , Kovscek Anthony R.
Published: April 2021

Heavy oil resources are becoming increasingly important for the global oil supply, and consequently there has been renewed interest in techniques for extracting heavy oil. Among these, in-situ combustion (ISC) has tremendous potential for late-stage heavy oil fields, as well as high viscosity, very deep, or other unconventional reservoirs. A critical step in evaluating the use of ISC in a potential project is developing an accurate chemical reaction model to employ for larger-scale simulations. Such models can be difficult to calibrate, however, that in turn can lead to large errors in upscaled simulations. Data-driven models of ISC kinetics overcome these issues by foregoing the calibration step and predicting kinetics directly from laboratory data.

In this work, we introduce the Non-Arrhenius Machine Learning Approach (NAMLA). NAMLA is a machine learning-based method for predicting O2 consumption in heavy oil combustion directly from ramped temperature oxidation (RTO) experimental data. Our model treats the O2 consumption as a function of only temperature and total O2 conversion and uses a locally-weighted linear regression model to predict the conversion rate at a query point. We apply this method to simulated and experimental data from heavy oil samples and compare its ability to predict O2 consumption curves with a previously proposed interpolation-based method. Results show that the presented method has better performance than previously proposed interpolation models when the available experimental data is very sparse or the query point lies outside the range of RTO experiments in the dataset. When available data is sufficiently dense or the query point is within the range of RTO curves in the training set, then linear interpolation has comparable or better accuracy than the proposed method. The biggest advantage of the proposed method is that it is able to compute confidence intervals for experimentally measured or estimated O2 consumption curves. We believe that future methods will be able to use the efficiency and accuracy of interpolation-based methods with the statistical properties of the proposed machine learning approach to better characterize and predict heavy oil combustion.
An Experimental Study of Steam-Assisted Gravity Drainage Using A Large-Scale Physical Model
Authors: Sheng Kai , Okuno Ryosuke , Imran Muhammad , Yamada Tomomi
Published: April 2021

This paper presents a large-scale experimental study of the compositional effect on produced bitumen properties in SAGD. The SAGD experiment used a sandpack in the cylindrical pressure vessel that was 1.22 m in length and 0.425 m in internal diameter. The pore volume of the sandpack was 58 L, and the porosity and permeability were 0.33 and 5.5 D, respectively. The sandpack was initially saturated with 93% bitumen and 7% deionized water.

The SAGD experiment after preheating was operated mostly at a steam injection rate of 35 cm3/min (cold-water equivalent) at 3600 kPa (244°C). The produced fluids (gas, oil, and water) were analyzed; e.g., ten oil samples were analyzed in terms of carbon number distribution (CND), the asphaltene content, density, and viscosity to investigate the compositional change of the produced bitumen. After the experiment, the sandpack was excavated and samples were taken for analysis of solid, water, oil, asphaltene, and sulfur contents. Experimental data (e.g., propagation of a steam chamber and production of oil and water) were history-matched by using a numerical reservoir simulator.

Results showed that the produced bitumen was lighter and contained 1 to 5 wt% less asphaltenes than the original bitumen. Also, the remaining oil inside the steam chamber contained 6 wt% more asphaltenes. As a result, the produced bitumen was 1 to 6 kg/m3 less dense than the original bitumen. In the actual operations, bitumen is diluted with condensate to reduce the oil viscosity for pipeline shipping. This reduction in bitumen density corresponds to a reduction of the diluent cost by 5-10%. The produced bitumen became less dense with increasing steam-chamber volume. The history-matched simulation indicated that the progressively decreasing density of the produced bitumen can be attributed to the vaporization of the relatively volatile components in the remaining oil, and condensation of those components near the chamber edge. The history-matching also indicated that varying flow regimes (counter-current and co-current flow of water and oil) affected the oil recovery during in the SAGD experiment.
Deep Learning for Downhole Data Prediction: A Cost-Effective Data Telemetry Through Data Analytics
Authors: Thakur Bharat , Samuel Robello
Published: April 2021

Accurate real-time downhole data collection provides a better understanding of downhole dynamics and formation characteristics, which can improve wellbore placement and increase drilling efficiency by improving the rate of penetration (ROP) and reducing downtime caused by tool failure. High-speed telemetry through wired drill string has enabled real-time data acquisition, but there are significant additional costs associated with the technology. Data-driven techniques using recursive neural networks (RNN) have proven very efficient and accurate in time-series forecasting problems. In this study, we propose deep learning as a cost-effective method to predict downhole data using surface data.

Downhole drilling data is a function of surface drilling parameters and downhole conditions. The downhole data acquired using relatively inexpensive methods usually have a considerable lag time depending on the signal travel length. So, the first step in the proposed method is syncing the downhole data and surface data. After the data are synced, they are then fed into an RNN-based long-term short memory (LSTM) network, which learns the relationship between the surface parameters and downhole data. LSTM networks can learn long-term relationships in the data, thus making them ideal for time-series forecasting applications. The trained model is then used to make predictions for downhole data using the given surface data.

The median error for the prediction of downhole data using surface data was as low as 3% in this study. The study suggests that the developed model can accurately predict downhole data in real-time. The model is also very robust to the amount of noise or outliers present in the data and can predict downhole conditions 50–60 ft ahead with reasonable accuracy. It was observed that the prediction accuracy varied from well to well and drilling depths. The results demonstrate how deep learning can be cost-effectively employed for downhole data prediction.

This paper presents a novel method for using surface data to predict downhole data by employing deep learning. The method can be deployed in real-time to aid in wellbore placement and improve drilling performance.
The Essence of Horizontal Drilling Challenges in Depleted Reservoirs
Authors: Elgaddafi Rida Mohamed , Soriano Victor , Ahmed Ramadan , Osisanya Samuel
Published: April 2021

Horizontal well technology is one of the major improvements in reservoir stimulation. Planning and execution are the key elements to drill horizontal wells successfully, especially through depleted formations. As the reservoir has been producing for a long time, pore pressure declines, resulting in weakening hydrocarbon-bearing rocks. Drilling issues such as wellbore stability, loss circulation, differential sticking, formation damage remarkably influenced by the pore pressure decline, increasing the risk of losing part or even all the horizontal interval. This paper presents an extensive review of the potential issues and solutions associated with drilling horizontal wells in depleted reservoirs.

After giving an overview of the depleted reservoir characteristics, the paper systematically addresses the major challenges that influence drilling operations in depleted reservoirs and suggests solutions to avoid uncontrolled risks. Then, the paper evaluates several real infill drilling operations through depleted reservoirs, which were drilled in different oilfields. The economic aspect associated with potential risks for drilling a horizontal well in depleted reservoirs is also discussed. The most updated research and development findings for infill drilling are summarized in the article.

It is recommended to use wellbore strengthening techniques while drilling a horizontal well through highly depleted formations. This will allow using higher mud weight to control unstable shales while drilling through the production zone. Managed Pressure Drilling should be considered as the last option for highly depleted formations because it will require a greater level of investment which is not going to have a superior rate of return due to the lack of high deliverability of the reservoir. Using rotary steerable systems is favored to reduce risks related to drilling through depleted formations. Precise analysis of different drilling programs allows the drilling team to introduce new technology to reduce cost, improve drilling efficiency and maximize profit. It is the responsibility of the drilling engineer to evaluate different scenarios with all the precautions needed during the planning stage to avoid unexpected issues.

The present market conditions and the advancement in technologies for drilling horizontal wells increase the feasibility of producing the depleted reservoirs economically. This paper highlights the challenges in drilling horizontal wells in highly depleted reservoirs and provides means for successfully drilling those wells to reduce risks while drilling
Evaluation of Hydraulic Fracturing Treatment with Microseismic Data Analysis in A Marcellus Shale Horizontal Well
Authors: El Sgher Mohamed , Aminian Kashy , Ameri Samuel
Published: April 2021

The objective of this study was to perform an integrated analysis to gain insight for optimizing fracturing treatment and gas recovery from Marcellus shale. The analysis involved all the available data from a Marcellus Shale horizontal well which included vertical and lateral well logs, hydraulic fracture treatment design, microseismic, production logging, and production data. A commercial fracturing software was utilized to predict the hydraulic fracture properties based on the available vertical and lateral well logs data, diagnostic fracture injection test (DFIT), fracture stimulation treatment data, and microseismic recordings during the fracturing treatment. The predicted hydraulic fracture properties were then used in a reservoir simulation model developed based on the Marcellus Shale properties to predict the production performance.

In this study, the rock mechanical properties were estimated from the well log data. The minimum horizontal stress, instantaneous shut-in pressure (ISIP), process zone stress (PZS), and leak-off mechanism were determined from DFIT analysis. The stress conditions were then adjusted based on the results of microseismic interpretations. Subsequently, the results of the analyses were used in the fracturing software to predict the hydraulic fracture properties. Marcellus Shale properties and the predicted hydraulic fracture properties were used to develop a reservoir simulation model. Porosity, permeability, and the adsorption characteristics were estimated from the core plugs measurements and the well log data. The image logs were utilized to estimate the distribution of natural fractures (fissures). The relation between the formation permeability and the fracture conductivity and the net stress (geomechanical factors) were obtained from the core plugs measurements and published data. The predicted production performance was then compared against production history.

The analysis of core data, image logs, and DFIT confirmed the presence of natural fractures in the reservoir. The formation properties and in-situ stress conditions were found to influence the hydraulic fracturing geometry. The hydraulic fracture properties are also impacted by stress shadowing and the net stress changes. The production logging tool results could not be directly related to the hydraulic fracture properties or natural fracture distribution. The inclusion of the stress shadowing, microseismic interpretations, and geomechanical factors provided a close agreement between the predicted production performance and the actual production performance of the well under study.