bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development of Time Lapse VSP Integration Workflow: A Case Study at Farnsworth CO2-EOR Project
Authors: McMillan Marcia , Will Robert , Bratton Tom , Ampomah William , Khaniani Hassan 
Published: October 2021

Abstract
This study aims to develop a 4D Vertical Seismic Profile (VSP) integration workflow to improve the prediction of subsurface stress changes. The selected study site is a 5-spot pattern within the ongoing CO2-EOR operations at the Farnsworth Field Unit FWU in Ochiltree County, Texas. The specific pattern has undergone extensive geological and geomechanical characterization through the acquisition of 3D seismic data, geophysical well logs, and core.
This workflow constrains a numerical hydromechanical model by applying a penalty function formed between "modeled" versus "observed" time-lapse compressional and shear seismic velocity changes. Analyses of geophysical logs and ultra-sonic measurements on core exhibit measurable sensitivities to changes in both fluid saturation and mean effective stress. These data are used to develop a site-specific rock physics model and stress-velocity relationship, which inform the numerical models used to generate the "modeled" portion of the penalty function. The "observed" portion of the penalty function is provided by a novel elastic full-waveform inversion of the available 3D baseline and three monitor surveys to produce high-quality estimates of time-lapse compressional and shear seismic velocity changes.
The modeling workflow accounts sequentially for fluid substitution and stress impacts. Hydrodynamic and geomechanical properties of the 3D coupled numerical model are estimated through geostatistical integration of well log and core data with 3D seismic inversion products. Changes in seismic velocities due to fluid substitution are computed using the Biot-Gassmann workflow and site-specific rock physics. Stress impacts on time-lapse seismic velocity changes are modeled from the effective stress output of the hydromechanical model and are initially based on the velocity versus effective stress relationship extracted from core mechanical testing.
Based on the principle of superposition of seismic wavefields, seismic velocity changes attributed to fluid substitution and that due to changes in mean effective stress are treated as linearly additive. The modeled results are upscaled using Backus averaging to reconcile scale discrepancies between the modeled and measured datasets to formulate the penalty function.
This manuscript presents the forward modeling process and concludes that for the base case, the seismic velocity changes due to mean effective stress dominates over the seismic velocity changes attributed to fluid substitution because of the extensive range of the pressure perturbations. Successful minimization of this penalty function calibrates the coupled hydrodynamic geomechanical numerical model and affirms the suitability of acoustic time-lapse measurements such as 4D-VSP for geomechanical calibration.
Integrated Acid Fracture Model with Reservoir Simulation Under Non-Isothermal Condition
Authors: Tajima Tohoko , Daniel Hill A. , Zhu Ding 
Published: October 2021

Abstract
Modeling of acid fracturing process is challenging because of the coupled complex effects of flow through porous media and fractures, chemical reaction in a geostatistical base, wormhole propagation, and reservoir heterogeneity. To avoid the complexity, decoupled approaches are commonly used; the reservoir effect is represented by leakoff with a constant leakoff coefficient, and analytical solutions for heat flux from a reservoir is used to avoid complexity. An acid fracturing numerical model is presented that is coupled with a single-phase black oil reservoir simulator for a vertical well in the carbonate reservoir. The coupled acid fracturing model considers fracture propagation, acid transport, and heat transfer. After simulating acid fracturing, the conductivity of the fracture is calculated using empirical correlations, and the productivity is computed by simulating the flow to the well. Non-isothermal condition is assumed to simulate the flow in both the fracture and reservoir because the acid reaction is temperature sensitive. Leakoff from fracture to reservoir is simulated with a reservoir flow model for pressure and leakoff velocity as functions of time and location. Wormhole propagation from the fracture is considered by using empirical equations for wormhole propagation based on leakoff velocity estimated from the reservoir simulation.
The benefits of coupled modeling are evaluated by comparing the conventional acid fracturing model which uses a decoupled approach to the numerical acid fracturing model developed in this study. The results show that the coupling reservoir model improves the accuracy of estimated in fracture conductivity. It has been shown that the analytical equations for heat from a reservoir used in literature overestimates the final acid fracture conductivity. Thus, it is suggested to use fully numerically solve fluid flow and energy balance in a fracture and a reservoir.
Complex leakoff due to pressure and temperature change with time and wormhole propagation was implemented in the simulator. The wormhole effect was added and the distribution of leakoff coefficient was reasonable. A comparison of simulation results with and without wormholes showed that the significant difference was not observed in acid concentration, but ideal width distribution was lower with wormholes. It is concluded based on the observation of the study that the leakoff from acid fracture represented by a reservoir model with wormhole propagation is important to correctly understand acid fracture efficiency. Simply using a constant leakoff coefficient can lead to significant error and misleading conclusions.
The Use of Controlled Dissolution Glass for the Consolidation of a High Porosity Chalk
Authors: Olsen Max , Hatlebakk Ragni , Holcroft Chris , Flatebø Roar Egil , Hoq Asif , Stavland Arne , Giske Nils Harald 
Published: October 2021

Abstract
This paper reports the development and testing, of a Phosphate controlled dissolution glass composition used to strengthen the matrix of chalk whilst retaining the permeability of the rock, facilitating improved hydrocarbon recovery in unstable wells.
Multiple versions of the glass solutions and different types of colloidal silica were extensively tested in the laboratory to determine injectability and reactivity with calcium carbonate rocks. The goal of the testing was to determine the best performing solution for use in a field trial in the Norwegian North Sea. The laboratory testing included filtration and core flood tests to determine the injectability of the solutions and post treatment permeability, and Brazilian strength tests to determine the tensile strength of the treated chalk cores. The filterability was tested through filter screen sizes ranging from 5 to 0.6 µm. Core flood testing was performed on 10 cm long chalk cores with 1.5 mD permeability.
The glass solutions showed the best results in the filtration and core flood testing, achieving significantly greater invasion depth than any of the colloidal silica samples. The phosphate glass treated chalk cores maintained 70 to 100% of the original permeability while delivering a 3 to 5 fold tensile strength increase. The lab tests demonstrated the potential of a glass based treatment to strengthen chalk formations without impeding permeability.Based on the promising results from the lab tests, it was decided to trial the selected glass solution in a mature vertical proppant fractured well. The test confirmed that the glass solution could be pumped into the well, but the test failed pre-maturely after two months of varied production, and the trial will not be covered in this paper.However, due to the high value in being able to stabilize chalk in the field, the Operator is evaluating a new trial in a horizontal well, and learnings from the first trial will be used to inform further lab tests in the next phase.
The glass solution used in this trial is being further developed to be used in other formation types, such as sand and non-calcium containing reservoirs.
Indirect Estimation of Clastic Reservoir Rock Grain Size from Wireline Logs Using a Supervised Nearest Neighbor Algorithm: Preliminary Results
Authors: Anifowose Fatai Adesina , Mezghani Mokhles Mustafa , Shahrani Saeed Saad 
Published: October 2021

Abstract
Reservoir rock textural properties such as grain size are typically estimated by direct visual observation of the physical texture of core samples. Grain size is one of the important inputs to petrophysical characterization, sedimentological facies classification, identification of depositional environments, and saturation models. A continuous log of grain size distribution over targeted reservoir sections is usually required for these applications. Core descriptions are typically not available over an entire targeted reservoir section. Physical core data may also be damaged during retrieval or due to plugging.
Alternative methods proposed in literature are not sustainable due to their limitations in terms of input data requirements and inflexibility to apply them in environments with different geological settings. This paper presents the preliminary results of our investigation of a new methodology based on machine learning technology to complement and enhance the traditional core description and the alternative methods. We developed and optimized supervised machine learning models comprising K-nearest neighbor (KNN), support vector machines (SVM), and decision tree (DT) to indirectly estimate reservoir rock grain size for a new well or targeted reservoir sections from historical wireline logs and archival core descriptions.
We used anonymized datasets consisting of nine wells from a clastic reservoir. Seven of the wells were used to train and optimize the models while the remaining two were reserved for validation. The grain size types range from clay to pebbles. The performance of the models confirmed the feasibility of this approach. The KNN, SVM, and DT models demonstrated the capability to estimate the grain size for the test wells by matching actual data with a minimum of 60% and close to 80% accuracy. This is an accomplishment taking into account the uncertainties inherent in the core analysis data. Further analysis of the results showed that the KNN model is the most accurate in performance compared to the other models.
For future studies, we will explore more advanced classification algorithms and implement new class labeling strategies to improve the accuracy of this methodology. The attainment of this objective will further help to handle the complexity in the grain size estimation challenge and reduce the current turnaround time for core description.
Development of a Gas Flow Rate Model for Multi-Stage Choke System in HPHT Gas Wells Using Artificial Intelligence
Authors: alghamdi Ahmed Abdullah , Almutairi Nawaf Saud , Muslim Ali , Khaldi Humoud , Abdulraheem Abdulazeez 
Published: October 2021

Abstract

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

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

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

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

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

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

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

Abstract
Fiber-optic cables cemented outside of the casing of an unconventional well measure cross-well strain changes during fracturing of neighboring wells with low-frequency distributed acoustic sensing (LF-DAS). As a hydraulic fracture intersects an observation well instrumented with fiber-optic cables, fracture fluid injected at ambient temperatures can cool a section of the sensing fiber. Often, LF-DAS and distributed temperature sensing (DTS) cables are run in tandem, enabling the detection of such cooling events. The increasing use of LF-DAS for characterizing unconventional hydraulic fracture completions demands an investigation of the effects of temperature on the measured strain response by LF-DAS. Researchers have demonstrated that LF-DAS can be used to extract the temporal derivative of temperature for use as a differential-temperature-gradient sensor. However, differential-temperature-gradient sensing is predicated on the ability to filter strain components out of the optical signal.
In this work, beginning with an equation for optical phase shift of LF-DAS signals, a model relating strain, temperature, and optical phase shift is explicitly developed. The formula provides insights into the relative strength of strain and temperature effects on the phase shift. The uncertainty in the strain-rate measurements due to thermal effects is estimated. The relationship can also be used to quantify uncertainties in differential-temperature-gradient sensors due to strain perturbations. Additionally, a workflow is presented to simulate the LF-DAS response accounting for both strain and temperature effects. Hydraulic fracture geometries are generated with a 3D fracture simulator for a multi-stage unconventional completion. The fracture width distributions are imported by a displacement discontinuity method program to compute the strain-rates along an observation well. An analytic model is used to approximate the temperature in the fracture. Using the derived formulae for optical phase shift, the model outputs are then used to compute the LF-DAS response at a fiber-optic cable, enabling the generation of waterfall plots including both strain and thermal effects.
The model results suggest that before, during, and immediately following a fracture intersecting a well instrumented with fiber, the strain on the fiber drives the LF-DAS signal. However, at later times, as completion fluid cools the observation well, the temperature component of the LF-DAS signal can equal or exceed the strain component. The modeled results are compared to a published field case in an attempt to enhance interpretation of LF-DAS waterfall plots. Finally, we propose a sensing configuration in order to identify the events when "wet fractures" (fractures with fluids) intersect the observation well.