bluuebunny/crossref_metadata_2025_split huggingface dataset:
3-Axis Borehole Gravity: Method and Application to CO2 Storage Monitoring and Oil/Gas Production
Authors: Topham Adrian , Lofts Jeremy , Du Zhijun , Espie Tony , Zett Adrian , Krahenbuhl Richard , Li Yaoguo 
Published: October 2020

Abstract
In the attempt to fill the scale gap between pulsed neutron and 4D seismic geophysical techniques to monitor underground reservoirs for ongoing oil/gas productionor CO2capture and storage, we introduce an emerging 3-axis borehole gravity technology that enables the recording of gravitational acceleration at high sensitivity, targeted at5 µGal.
This is made possible using an innovation in resonant Microelectromechanical systems (MEMS) vibrating beam technology. This technology is designed to sense gravitational field produced by mass density changes in the subsurface, such that time-lapse wireline-based surveys may be taken to image fluid movements as far as 100s of meters from a wellbore and thereby enable time-lapse or 4D gravity monitoring. The innovation of 3-axis gravity measurement allows the acquisition of directional information about the spatial movement of fluid, even when acquired from just a single borehole.The cost effectiveness of a 4D wireline gravity survey compared to 4D seismic survey is highly attractive especially in the later stages of production surveillance programs, or as a complementary survey.
We will first introduce the technology, and then present its application through a feasibility study aimed at the monitoring of CO2 in a deep storage reservoir in Canada. We model and predict the gravity variation in a 4D gravity surveylikely to be seen due to density changes during a period of CO2 injection at the storage site. Survey feasibility modelling and a workflow are presentedthat together provide important information forplanning and acquiring successful 4D gravity surveys, including the optimal time intervalbased on the planned injection rateand the optimal well location to use.
The study will illustrate the general use for of time-lapse 3-axis gravity in monitoring reservoirs for optimising production over time while additionalexamples will be shown to further demonstrate application within oil/gas production reservoirs.
Synergy Between Commodity Molecules and Nanoparticles as Steam Mobility Control Additives for Thermal Oil Recovery
Authors: Fehr Adam , Telmadarreie Ali , Berton Paula , Bryant Steven 
Published: October 2020

Abstract
Achieving mobility control of steam in porous media has been approached by the implementation of foaming surfactant additives; however, foam generated with surfactants lack stability at high temperatures (≥ 200 °C). In this study, a nanofluid was formulated for steam co-injection to address common issues with surfactants as steam additives. The nanofluids were formulated using synergistic interaction of nanoparticles and surfactants (both readily available) to address thermal stability, and foam stability at the conditions encountered in thermal enhanced oil recovery processes such as steam assisted gravity drainage.
Zeta potential analysis, static tests, and high temperature aging tests were performed to obtain the ideal mixture of nanoparticles and surfactants. A steam/foam core flooding apparatus was used to evaluate the mobility reduction of nanofluids using multiple differential pressure transmitters situated along the length of a 40 cmsand pack to confirm foam propagation. The tests were first conducted at 200 °C with nitrogen as a non-condensable gas carrier. Upon confirming nanofluid mobility reduction characteristics at 200 °C with gas, a successful nanofluid was co-injected with superheated steam at backpressure corresponding to a saturation temperature of 200 °C inside an oven with that set point. Mobility reduction characteristics were obtained for each formulation by normalizing the differential pressure of additive multiphase flow to their respective baselines.
Statictests and high-temperature core floods demonstrated a strong synergy between appropriate combinations of surfactants and nanoparticles.None of thesurfactants and nanoparticlesyielded mobility reduction when used on their own; however, when selected according to a design basis, nanoparticle/surfactant mixtures exhibit strong mobility control with steam as well as hot gas. The foam produced in the porous media was held in a visual cellat 150 °C for 24 h with no loss in foam height. Furthermore, foam generated with the nanoparticle-surfactant hybrid remained stable in static tests in the presence of heavy crude oil for more than one week.
The primary novelty of this study is the ability to use less exotic surfactants and nanoparticles as steam mobility control agents, increased foam stability in the presence of oil, and demonstrable synergy between commodity molecules (i.e., surfactant) and nanoparticles at steam flooding conditions. The successful additive mixture was developed with scalability in mind such that manufacturing will not hinder the feasibility of scale-up for industrial use.
Reducing the Volume of Water Needed for Hydraulic Fracturing by Employing Natural Gas Foamed Stimulation Fluid
Authors: Malpani Raj , Daeffler Chris , Verma Sandeep , Beck Griffin , Ogunyemi Taofeek 
Published: October 2020

Abstract
As part of a Department of Energy (DOE)-sponsored program, an optimal thermodynamic pathway to transform natural gas (NG) into pressurized NG suitable for use as the internal phase in a foamed fracturing fluid has been already developed. Using NG foamed fracturing fluids reduces the enormous water requirements for stimulation by as much as 60% to 80% and poses benefits for productivity in water-sensitive formations. This study aims to extend the investigation to characterize the hydraulic fracture geometry and quantify the expected production when using an NG foam fracturing fluid. Using validated models, we provide a comparative analysis to determine the advantages of using natural gas foams relative to conventionally used slickwater, linear gel, and crosslinked fluid.
A full 3D reservoir model in a Duvernay Shale formation was constructed. Fundamental laboratory and pilot field tests data was collected for the NG foam fluid properties for numerical modeling. Rheology, friction, and leakoff properties of the fracturing fluids were incorporated in creating a numerical model. A 3D-complex hydraulic fracture simulation model incorporating 1D and 2D particle transport models were used. A numerical reservoir simulation for different sensitivity scenarios was incorporated for fracture modeling and gas production evaluation. Owing to lower density than conventional liquid column, the NG-foamed fluids are likely to result in higher surface pressure.
A reduced pump rate with NG foamed fracturing fluid leads to a lower frictional pressure loss in tubing, without compromising the ability to place the desired amount of proppant in the formation. The non-Newtonian shear-thinning NG-foamed fracturing fluid exhibits a higher, effective viscosity that enables effective transport of the proppant. Modeling results indicate that the overall fracture geometry and proppant placement is much better for NG-foamed fluids than high-volume slickwater needed to pump the same amount of proppant for well spacing and a field development plan. The simulated production performance for medium-viscosity fluids such as NG-foamed fluid, linear gel, and high-viscosity slickwater, is better than that of low-viscosity slickwater or high-viscosity crosslinked gel fluids. A low-viscosity fluid results in proppant settling and dunning, resulting in lower conductive height of fractures while the high viscosity treatment uses less fluid, so the surface area created is less and there is potential for height grow out of the target formation.
Complex Fracture Hits Modeling and Analysis for Parent-Child Wells in Eagle Ford
Authors: Yu Wei , Fiallos Torres Mauricio Xavier , Liu Chuxi , Miao Jijun , Sepehrnoori Kamy 
Published: October 2020

Abstract
Shale field developmentfinds significant challenges when operators have to define optimal spacing of infill wells and further fracture optimization, based on biased understanding of the physical phenomena behind fluid flow in complex unconventional reservoir systems. Although proper modeling has been employed in other studiesto address the detrimental impact of well interference, this study poses how these fracture hits can be beneficial after estimating their impacts in hydrocarbon cumulative recoveries. This study includes spatial variations in fracture conductivity and complexity on fracture geometries of inter-well interference. Furthermore, a non-intrusive embedded discrete fracture model (EDFM) method has been employed to generate these complex scenarios and investigate the impact of well interference multi-well field models. Based on a robust understanding of fracture properties, real production data and wellbore image logging, multiple comparison are performed to address the effects of accounting for inter-well fracture hits on field pressure and production response. First, according to updated production data from Eagle Ford, a model was constructed to perform two (parent) wells history matching. Later, three child wells were included so thatoptimal cluster spacing was recommended considering interwell interference and the distance to thoselong-induced fracture hits. Finally, a field case is presented where the effects of long interwell fractures are evaluated in a nine-well numerical model and contrasted to a scenario without fracture hits. This case is an extension of the work presented by Fiallos et al. (2019) where fracture diagnostic results from well image logging were employed to perform sensitivity analysis on attributes of long interwell connecting fractures.
The simulation results show that long induced fracture hits can be addressed by correlating inter-well wellbore image logs, which will support the occurrence of well interference. Because of these interwell long fracture hits, favorable communication is originated and, thereby, it enhances the oil recovery of the child wells by expanding their drainage influence towards further zones of the reservoir. Likewise, the higher permeabilities in this fracture hits reduce the bottomhole pressure drawdown. As a consequence, the model became a valuable stencil to decide the cluster spacing, and to optimize the hydraulic fracture treatment design. The simulation results were applied to the field successfully to afford significant reductions in offset frac interference by up to 50%.
Nanoparticle-Stabilized Strong Foam for EOR in Fractured Reservoirs
Authors: Xuezhen Wang , Mohanty Kishore 
Published: October 2020

Abstract
Many carbonate reservoirs are fractured. Injection of EOR agents (miscible gases or surfactant solutions) into fractured reservoirs leads to flow through the fractures and bypassing of the matrix. These fluids can be directed into the matrix if the fluids are injected into the fractures as a foam. The goal of this work is to develop a strong foam for injection into fractured reservoirs by using nanoparticles. Ethyl cellulose nanoparticles (ECNP) were prepared from ethyl cellulose with 48% ethoxy groups. ECNP were used to stabilize foam with and without a nonionic surfactant, CH3-70PO-100EOH. The size of the ECNP prepared was smaller than 200 nm with a narrow size distribution. The addition of ECNP to a nonionic methoxy surfactant (CH3O-70PO-100EOH) helped with foamability and foam stability compared to surfactant only samples. The foam stability in the presence of oil was also improved by ECNP. Foam flooding with an oil-wet fractured Texas Cream limestone indicated that the oil recovery was much higher with ECNP/nonionic surfactant compared to a conventional AOS foam and also the immiscible gas (methane/ethane gas mixture at a molar ratio of 1:1) alone. The oil recovery was 71% with the injection of 80% quality foam using 500 ppm ECNP and 1.5 wt% nonionic surfactant at 1wt% NaCl concentration. This work illustrates that the oil recovery can be improved with a strong foam even with gases that are not multi-contact miscible.
Automated Procedure for Quantifying ISIP and Friction Losses from Stage by Stage Hydraulic Fracture Treatment Falloff Data
Authors: Alwarda Fahad , Liu Guoqing , Nikolaou Michael , Ehlig-Economides Christine 
Published: October 2020

Abstract
While diagnostic fracture injection tests (DFIT) data is relatively rare, most hydraulic fracture treatment stages in multiple transverse fracture horizontal wells (MTFHWs) follow pressure and rate data during pumping with several minutes of pressure falloff data after the end of pumping. Recent papers have shown value in applying analysis developed for DFIT data to hydraulic fracture treatment falloff (HFTF) data. As is often the case with routinely acquired operational data, the time required for analyzing HFTF datasets for each of the treatment stages in a long horizontal well may be prohibitive. This paper offers an automated analysis procedure that starts from standard treatment pressure and rate data and produces estimates for wellbore and perforation friction loss, near wellbore tortuosity friction loss, and the instantaneous shut in pressure (ISIP).
Steps in the automated procedure include isolating the HFTF data from the rest of the hydraulic fracture treatment data (which is typically subject to hydraulic hammer), applying an optimized low-pass filter (LPF), and computing the friction and ISIP estimates by automating a previously published graphic procedure. We employ the automated analysis on field data that was previously analyzed by hand. Then we compare and contrast between the two analyses.
The comparison between manual and automatically analyzed fracture treatment falloffs demonstrates that the automated procedure reproduces the previous analyses except in a few cases that pose special challenges to any analysis. The new field dataset results demonstrate the approach is practical for field application.
Variations in friction loss and ISIP estimates along MTFHWs provide data useful for well and pad completion design decisions related to perforating strategies, and fracture and well spacing.
Mini Fracs Can Provide Reliable Formation Permeability in Unconventional Shale Reservoirs
Authors: Uzun Ozan , Uzun Ilkay , T. AlHashmi Omar , Kazemi Hossein 
Published: October 2020

Abstract
Mini frac, or diagnostic fracture injection test (DFIT), is a short hydraulic fracturing test that provides formation break-down pressure, minimum horizontal stress, and reliable value for formation permeability of shale reservoirs. The calculated formation permeability is particularly more reliable from the analysis of the second cycle of the mini frac test because the fracture is already created. In this paper, we present a simple technique to analyze and interpret DFIT data similar to the analysis of the classic drillstem test (DST) data in vertical wells. The only difference is that in DFIT pressure-time characteristics approximate the linear flow regime while in DST, pressure-time behavior follows the radial flow regime.
In general, DFIT analysis provides matrix permeability while the analysis of long-term pressure decline of the production data yields stimulated formation permeability, which can be attributed to microfracture permeability. Thus, we will show how we utilize the permeability from DFIT, and the permeability calculated from the production decline data of production wells (i.e., multistage hydraulically fractured wells) to construct a viable dual-porosity model to assess the performance of wells under primary production and gas injection EOR. We also compare our results with those of Nolte G-function.
The paper includes numerical modeling of two-phase nonlinear flow, analytical solution methodology from multi-phase flow, experimental data, and field data to illustrate the viability of our interpretation method. Furthermore, our analysis technique is simple because it only uses pressure falloff data points during the shut-in period of the DFIT. Not only the method is confirmed by numerical modeling, but it is also verified by pressure falloff from laboratory data.
New-Type Water Controlling Technology for Improving Production Performance of Offshore Heavy-Oil Reservoir with Active Bottom Water
Authors: Shang Baobing , Liu Yigang , Meng Xianghai , Bai Jianhua , Wu Huaxiao , Yu Fahao , Mu Mei , Li Yue 
Published: October 2020

Abstract
This paper introduces a new water controlling technology to manage water production and improve production performance in an offshore oilfield in Bohai, China. Q oilfield is a typical heavy oil reservoir, mainly developed by horizontal wells. One of the main challenges for the production is the high water cut. Most of the wells experience water breakthrough within one year after put into production and produce for long time with water cut higher than 90%. Besides, the strong anisotropism of the formation aggravates the water breakthrough and makes water control work more difficult.
In 2019, a new combined water control technology was applied to manage water influx in horizontal completions. In this technology, the annulus between the wellbore and the inner ICD (inflow control device) /AICD (Autonomous inflow control device) screen is filled with light-but-hard particles. In this paper, the barrier built by the fine particles is called continuous-packer. The existence of this barrier plays a similar role to mechanical packers between each ICD/AICD screen, thus the axial flow of the produced liquid is prevented in the horizontal section. Besides, the ICD/AICD screen is equipped to limit the liquid inflow of each segment based on the design. The purpose of equalizing production profile of the horizontal section achieves through the cooperation of the continuous-packer and the ICD/AICD screen.
Untill now, this new technology has been used in more than 6 wells in Q oilfield, including both producing horizontal wells with high water cut and newly drilled wells. The production results show that the water cut reduces about 10% and the oil production increases for the high water cut well. The water breakthrough time and the water cut increasing rate is slower for the new wells comparing with near wells.
The successful application of this technology demonstrates its validity for the offshore heavy-oil reservoir with active bottom water. It also provides a new method for the water controlling work for the offshore wells in Bohai oilfield. A detailed plan has been finished to implement this technology in more wells in 2020.
Enhanced Wellbore Leak Localization with the Estimation and Removal of Guided Wave Noise Using Array Hydrophone Logging Data
Authors: Ge Yao , Wang Ruijia , Ang Yi Yang , Tracadas Philip , Wu Xiang , Padhi Amit 
Published: October 2020

Abstract
Leaks in wellbore tubulars emit acoustic waves in the borehole, which can be captured by a hydrophone array. Processing the array data yields location and energy level of the wellbore leaks; however, the hydrophones may also capture other coherent noise propagating as guided waves along the borehole, such as road noise caused by the displacement of logging equipment on the wellbore surface, by vibration from surface production facilities, and by distant loud leaks. Such coherent acoustic noise largely interferes with the estimation of a target leak's characteristics. Conventionally, to record higher-quality data, additional recordings may be required to station the tool at selected locations; however, this standard approach prolongs the acquisition time and constrains the vertical resolution.
This paper describes an advanced approach to estimate and subsequently remove the guided-wave noise from the array hydrophone data to improve the accuracy of leak source locations. Furthermore, estimating the propagation direction and amplitude of leak-induced guided waves aids logging operations to efficiently locate a leak source.
We propose an array data processing approach to separate the guided-wave noise from the direct arriving leak signal. The upward and downward propagating guided waves are identified using a least-square wave separation method. An alternative time-domain stacking method is also proposed for real-time fast computation. The slowness of the guided wave is optimized within a range and then used to estimate and remove the guided waves. Furthermore, characteristics of the estimated guided-wave noise can be extracted, such as the propagation direction, amplitude, and slowness. These parameters plotted in discrete depth locations provide additional information on the well condition.
Synthetic and field data results show that the proposed method significantly improves the accuracy of the 2D flow map by removing the erroneous map artifacts due to guided waves. The low-frequency energy due to guided waves is also removed from the frequency spectrum log. The amplitude difference between the upward and downward propagating waves indicates the source direction of the leakage-induced waves. Hence, monitoring the guided wave amplitude and direction in real time provides an efficient way to quickly locate a leak source and reduce operation time.
Conventional noise logging data are commonly contaminated by guided-wave noise. Single hydrophone tools cannot separate out these guided waves. The proposed approach, using a hydrophone array for separating guided-wave noise from direct arriving leak signals, provides a high-quality estimation of the energy and location of leaks in wellbore tubulars.
A Self-Learning Process Modeling Method to Optimize Upstream Operations
Authors: Guddeti Ravikishan , Sankaran Sathish 
Published: October 2020

Abstract
Upstream production optimization is concerned with optimizing the entire hydrocarbon value chain from reservoir to sales. In this quest, managing the process facilities to maximize productivity is a critical part. Traditionally, operating companies have addressed it by running offline scenarios to develop a playbook or build physics-based dynamic simulation models. However, a typical offshore facility undergoes significant changes to its operations during its life cycle, which makes the model management tedious and expensive. Few attempts have been made to build data-driven digital twins (machine learning) but they often lack the ability to provide explainable models and lack physical insights. In this work, we propose to use a fast, hybrid, self-learning dynamic process modeling method from routine plant measurements that can be used for real-time forecasts, scenario modeling, process optimization and control.
A reduced order modeling method based on input-output dynamic mode decomposition (ioDMD) has been adapted to develop a dynamic process model based on historical data collected from plant sensors. First, we benchmarked the proposed approach with a dynamic simulation model (commercial simulator) using a designed input sequence for training. The ioDMD model simplifies the physical mechanisms to a low dimensional form. Next, we applied the method to an actual offshore deepwater facility based on plant measurements. In both cases, the ioDMD method provided very good predictions without any human intervention. Unlike black-box data-driven methods, the ioDMD method uses an interpretable approach that can be used to explain causal relationships. Observability and controllability of the proposed model can also be easily understood.
The proposed ioDMD method provides a unique and sustainable way to combine advanced analytics and physics to develop an explainable dynamic model for the process facility that can be effectively used to assist operations in optimizing performance. The lightweight model lends itself naturally to fast computation that are required for optimization and process control (including IoT edge devices).