bluuebunny/crossref_metadata_2025_split huggingface dataset:
Inverting Injection-Induced Microseismic Monitoring Data with Coupled Flow and Geomechanical Models: Application to CO2 Injection
Authors: Hakim-Elahi S. , Jafarpour B. 
Published: April 2018

Abstract
High pressure fluid injection into geologic formations can trigger rock deformation and failure and lead to microseismic events. Injection induced seismicity has been proposed as a monitoring technique that can be used to constrain the description of rock flow and mechanical properties. In this paper, we present a stochastic geomechanics-based approach to establish physical correlation between rock flow and mechanical properties and microseismic monitoring data during CO2 injection. The resulting correlations are then used to estimate rock properties from observed microseismic clouds. To establish the correlations between rock properties and microseismic clouds, the developed framework combines coupled flow and geomechanics simulation outputs with Mohr-Coulomb's failure criterion to describe the spatiotemporal distribution of seismicity potential in the formation (i.e., a map that quantifies the probability of geomechanically-induced seismic events at different times and locations in the formation). The resulting seismicity potential map depends on the stress conditions and mechanical strength of the formation rock and establishes a relationship between predicted microseismic clouds and rock property distributions. This model is then used with an ensemble-based data assimilation (i.e., ensemble smoother with multiple data assimilation, ES-MDA) to estimate rock properties form observed microseismic data. Since the ensemble smoother is designed for assimilation of continuous data, kernel density estimation (KDE) is applied to convert discrete microseismic events to a continuous map of seismicity density. Furthermore, for estimation of discrete geologic facies, prior to data assimilation, a distance transformation is used to convert facies descriptions to constinuous parameters. We present the developed formulation and discuss its application to inversion of microseismic data for characterization of reservoir flow and mechanical properties. Several examples are presented to evaluate the performance of the method for estimation of rock properties from microseismic data. Our preliminary results, based on synthetic experiments, suggest that the developed method can combine coupled physics models with microseismic monitoring data to constrain the description of rock flow and mechanical properties.
Validation and Improvement of the Horizontal Pipe Separator Model
Authors: Othman H. A. , Dabirian R. , Gavrielatos I. , Mohan R. , Shoham O. 
Published: April 2018

Abstract
Horizontal Pipe Separators (HPS©) are used for separation of oil from oil-water mixture. It can be an attractive alternative to the vessel type separator owing to its simplicity, ease of construction, installation and operation and its lower price. An experimental and theoretical investigation on the (HPS©) were conducted. New experimental data and mechanistic modeling are presented to show the effect of different variables such as mixture velocity and water cut on the performance of the separator.
A HPS facility, consisting of a 0.0762 m ID 10.3 m long horizontal pipe has been designed and constructed. Five observation boxes are installed along the horizontal pipe to observe the development of the oil/dispersion and water/dispersion interfaces throughout the pipe. A wide range of experimental data was acquired for oil-water mixture flow behavior, and length of entry region for the oil and water separation in the Horizontal Pipe Separator was also investigated. A total of 34 runs were conducted for mixture velocities of 0.08, 0.13, 0.20 and 0.30 m/s with water cuts between 10 to 90%.
The experimental data confirm that the higher the mixture velocity, the longer is the entry region required for separation. On the other hand, when increasing the water cut, the water separation is more efficient. Similarly, decreasing the watercut results in an easier separation of the oil phase. For low water cuts (10 to 30%) and higher mixture velocities (&gt;0.3 m/s), no separation between the phases was observed. The Gassies (2008) model has been validated and improved for water continuous flow by developing correlations for two of the Gassies model's input variables, namely, the turbulent decay time and the oil volume fraction in the dense packed zone. For water continuous phase, the comparison between the improved model prediction and the experimental data shows very good agreement for the water/dispersion interface and also for the oil/dispersion interface at low mixture velocities.
Lightweight and Ultra-Lightweight Cements for Well Cementing - A Review
Authors: Anya A. 
Published: April 2018

Abstract
Lost circulation is a very common and expensive problem during drilling and cementing operations in the oil and gas industry. The lost circulation problems encountered during drilling or cementing are a result of one of two factors. These factors are the presence of zones of weak fracture gradients and the presence of high permeability or thief zones downhole. Light weight cements are an effective solution for curbing lost circulation caused by the breakdown of weak zones by conventional cement slurries. This paper presents a discussion of the different methods, additives and technologies that have been and are currently employed in the formulation of lightweight and ultra-lightweight cement slurries for cementing oil wells as well as recent developments based on an extensive literature review. This paper also discusses the mechanical performance, cost effectiveness and field logistical considerations of lightweight slurries formulated using different methods as these are important factors that impact decision making on what slurry extension method to choose for any given scenario.
The information presented in this paper is derived from an extensive review of information contained in papers, journals and books spanning the last 50 years of well cementing and is summarized in such a way that the paper serves as a quick guide to cement slurry extension technology and techniques.
An extensive review of the literature regarding lightweight cements showed that there are three general methods of obtaining lightweight cement slurries. These methods include increasing cement slurry water content (water extension) with the aid of viscocifying agents such as bentonite and sodium metasilicate, adding lightweight materials like glass microspheres and incorporating foam into slurries. Reported test data shows that apart from cement slurries containing glass microspheres and foamed cements, lightweight slurries exhibit lower compressive strength and slower compressive strength development than heavy slurries. Foamed cements pose the greatest design and field logistics challenge while cement slurries with glass microspheres have gained more popularity due to the excellent compressive strength values achievable at ultra-low densities despite their higher cost compared to water-extended slurries and lightweight slurries containing other lightweight additives like fly ash.
The literature review presented also indicates a need for more research into improving lab mixing and testing methods that replicate field applications of foam cement. There is also a need for research into more cost-effective slurry extension additives and technology that exhibit acceptable mechanical performance for well integrity assurance and rheological properties favorable to proper cement placement in the annulus.
Infill Well Location Selection Procedures in Lost Hills Using Machine Learning
Authors: Kim T. H. , Crane D. J. , Grijalva E. F. 
Published: April 2018

Abstract
The diatomite reservoirs in Lost Hills field provide an interesting reservoir management challenge due to its unique natures such as low permeability (~ 1 md) but high porosity (~ 50%), weak rock strength, and strong imbibition. In most conventional reservoirs, numerical simulation has been successful technology in forecasting, history matching and extraction of valuable information on optimum new well locations placement. However, numerical simulation results of Lost Hills were not successful due to the unique characteristics of the diatomite reservoir noted above. Therefore, the current procedures of selecting new well locations are based on the surface patterns and static reservoir properties.
Machine learning (ML) has recently been spotlighted since it does not require specific physical models but can provide good estimations if there is enough data. Features like permeability, oil saturation, and other common reservoir properties also impact on production in diatomite reservoir, but the contribution of each property is not clearly understood. Because of its characteristics of not requiring physical models, ML has strong applicability in Lost Hills of which exact hydrocarbon production mechanisms are not clearly understood.
In this study, a new methodology using Machine Learning was introduced for infill well location selection. This approach led to identification of approximate 550 infill well candidate locations, which were recommended further to the asset development team for execution. The infill well location selection workflow consists of historic data analysis, drainage area estimation, ML model training, and candidate location estimation. Using K-means clustering and statistical means, the characteristics of good producers are analyzed. Then, the drainage area for each well is approximated using Voronoi cells. This allows a relative good estimation of the remaining oil in place and non-draining areas. A support vector machine (SVM) model was trained to rank the locations based on their production capabilities. The new workflow provides more systematic means on new well location selection and enable the asset team to make data driven decisions.
Interpreting Step Rate Tests SRTs in Multi-Layered, Unconsolidated and Stress-Sensitive Formations
Authors: Ershaghi Iraj , Ershaghi Milad A. 
Published: April 2018

Abstract
We studied the applicability of Step Rate Tests (SRTs) for estimation of suitable injection gradients and reviewed various diagnostic plots to examine their relationship with injection rate and pressure. While dealing with multi-layered systems and those consisting of unconsolidated formations and examining the causes of injection increase seen, we identified some shortfalls associated with the SRT procedure. We also examined the force balance of formation response to injection gradients and concluded that formation resistance to water injection changes with time. That means the injection gradient to maintain constant injectivity is affected by the near wellbore conditions and as such, has a dynamic nature and can change with time. For unconsolidated formations, changes in pore pressure may also affect the rock effective permeability which alone can influence the required injection gradients.
Advanced Analysis of Clean-Up and Productivity from Perforated Rocks Using Computational Fluid Dynamics
Authors: Satti Rajani , Zuklic Stephen N. , Bale Derek , Koliha Nils , Fager Andrew , Balasubramanian Gana , Crouse Bernd , Freed David 
Published: April 2018

Abstract
Perforated completions play a crucial role in efficient hydrocarbon recovery as well as long-term well productivity. However, perforating often alters formation permeability around the perforation tunnel, which along with other perforation parameters (penetration, hole size, and shot configuration) significantly impacts near-wellbore pressure drop, and therefore, production. Standard numerical or inflow production models are based on many assumptions involving tunnel geometry and clean-up, debris and damage characteristics. Such an approach often leads to discrepancies between estimated and actual productivity of the tunnel, thereby affecting the overall completion planning through the life-of-the-well.
In recent years, perforation flow laboratories have been leveraged to provide insight into clean-up and productivity mechanisms around perforation tunnels. However, the heterogeneity of the analog cores, challenges with isolating competing effects of debris/clean-up and limited amount of experimental data have led to the advent of computational methods. In contrast to previous studies, the model presented in this work leverages the perforation flow laboratory, micro-CT and conventional CT imaging, and most importantly, an advanced simulation approach to provide an accurate assessment of clean-up techniques and productivity. This full-scale three-dimensional flow model accounts for realistic aspects of tunnel geometry, perforation damage, and blockages/debris that impede the flow.
The simulation based approach facilitated a comprehensive understanding of the competing effects of perforation damage, debris within the tunnel, tunnel tip plugging, and directionality of flow around the tunnel. Whole-field flow visualization including fluid velocities, streamlines, pressure gradients, and most importantly, the flow rates and corresponding productivity ratio are presented and discussed in detail. Further, this study provided insight into quantitative comparisons of influence of debris and the overall effect of underbalance methods on clean-up and productivity. This work therefore enables the implementation of a multi-scale simulation approach as a practical engineering tool that can be used to provide a realistic prediction of downhole productivity as well as evaluating the effects of clean-up techniques. Such insight will eventually provide information that enhances the decision making process of a perforated completion design.
Pattern-Based History Matching for Reservoirs with Complex Geologic Facies
Authors: Golmohammadi A. , Khaninezhad M. R. , Jafarpour B. 
Published: April 2018

Abstract
History matching is performed to obtain reservoir models that reproduce the historical production data while adhering to available prior geologic knowledge and observed static data. In automated history matching workflows, prior models of reservoir properties are continuously updated to match the incoming production history. A challenging problem is to ensure that after applying updates to prior models, the resulting history matched models remain geologically consistent. This is particularly challenging in formations with complex connectivity patterns, e.g., fluvial meandering and curvilinear channels, where preserving the distinct shape and continuity of the underlying geologic features is non-trivial. In this work, we introduce a novel machine learning approach with the aim of preserving the main connectivity patterns of the prior reservoir models during history matching of complex geologic formations. We formulate the history matching problem by defining a feasible set of connectivity patterns that are described by a large number of model realizations. The feasible set encompasses the range of connectivity patterns of the expected geologic objects in the prescribed conceptual model by geologists. A supervised machine learning algorithm is then introduced to learn a mapping operator between any given model and its closest model in the feasible set. For this purpose, a learning dataset, i.e., a set of feature/label pairs, is constructed from the representative samples of the feasible set. The k-Nearest Neighbor (k-NN) classification algorithm is then applied to relate the local connectivity patterns in the feasible set that are closest to the patterns in a proposed model outside the feasible set. The learned mapping operator is invoked during history matching, where the misfit between model-predicted and observed historical production data is minimized while honoring the connectivity in the prior feasible set. The history matching is performed using a two-step alternating directions optimization algorithm, in which the first step implements a gradient-based continuous minimization procedure to decrease the data mismatch objective function while the second step maps the obtained solution from the first step onto the prior feasible set. History matching case studies in channelized reservoirs demonstrate that the proposed supervised learning approach can learn the complex geologic patterns in the feasible set and use them during history matching to preserve the feasible connectivity patterns. The results suggest that the proposed classification and clustering approach can facilitate patter-based history matching problems by learning geologic features from prior models and using them to impose geologic feasibility.
Low Salinity Hot Water Injection with Addition of Nanoparticles for Enhancing Heavy Oil Recovery under Reservoir Conditions
Authors: Ding Yanan , Zheng Sixu , Meng Xiaoyan , Yang Daoyong 
Published: April 2018

Abstract
In this study, a novel technique of low salinity hot water (LSHW) injection with addition of nanoparticles has been developed to examine the synergistic effects of thermal energy, low salinity water (LSW) flooding, and nanoparticles for enhancing heavy oil recovery, while optimizing the operating parameters for such a hybrid enhanced oil recovery (EOR) method. Experimentally, one-dimensional (1D) displacement experiments under different temperatures have been performed, while two types of nanoparticles (i.e., SiO2 and Al2O3) are respectively examined as the additive in the LSW. The performance of LSW injection with and without nanoparticles at various temperatures is evaluated, allowing optimization of the timing to initiate low salinity water injection. The corresponding initial oil saturation, production rate, water cut, and ultimate oil recovery, are continuously monitored and measured under various operating conditions. Compared to conventional water injection, the low salinity water injection is found to effectively improve heavy oil recovery as an EOR technique in the presence of nanoparticles. Also, the addition of nanoparticles into the LSHW can promote synergistic effect of thermal energy, wettability alteration, and reduction of interfacial tension (IFT), which improves water displacement efficiency and thus enhances oil recovery. It has been experimentally demonstrated that such LSHW injection with the addition of nanoparticles can be optimized to greatly improve oil recovery up to 40.2% in heavy oil reservoirs with low energy consumption.
Securing California Gas Supplies by Storage in Mature Fractured Monterey Reservoirs of Offshore California
Authors: Ershaghi Iraj , Paul Donald L. , Kaba Saran 
Published: April 2018

Abstract
In this paper we discuss our studies conducted on two California offshore fields that may be abandoned in near future. The purpose of the study was to examine the feasibility of re-purposing these fields to suitable offshore gas storage by utilizing the reservoir voidage and by using the existing pipeline facilities. These storage sites could offer a significant alternative to the current onshore sites located in highly populated urban areas of California.
Gas storage in certain California offshore fields producing from the fractured Monterey formation could eliminate the potential environmental risks associated with urban onshore storage of gas and prevent incidents such as the October 23, 2015 one in the Aliso Canyon Natural Gas Storage Facility in Los Angeles County. The 100,000 tonnes of methane emitted into the atmosphere resulted in the relocation of thousands of people from the areas proximal to the facility.
Study of the caprock and initial reservoir pressures encountered in these fields shows that a proposed 3000 psi storage pressure is safe for offshore storage purposes. Our computation of voidage caused by more than 3 decades of production shows that on a total basis, under a storage pressure cap of 3000 psi, these two fields together can help in storage of more than 3 TCF of gas. This is about 5 times the existing storage capacity of 0.6 TCF in California. On the long terms basis, the proposed offshore storage fields could provide a secure source of energy for the evolving market of California CNG based transportation, power generation and other consumer needs.
Modeling Pore Compressibility Effects in Unconventional Reservoir Performance Analysis: Application to Bakken Formation
Authors: Eker Erdinc , Uzun Ilkay , Kazemi Hossein 
Published: April 2018

Abstract
Unconventional reservoirs consist of matrix, micro-fractures, and macro-fractures. There is a significant difference between stress-dependent deformation of matrix rock and the fracture-containing reservoir frame. These differences are great enough to affect the long-term production performance of reservoir. Thus, accurate representation and modeling of such diverse characteristics are needed to predict reservoir performance. Changing reservoir pore pressure during production causes decrease in porosity and permeability and deforms the pore geometry. Thus, in mathematical modeling, reduction in pore volume is a rock deformation issue including the ever-changing pore compressibility. Typically, coupled geomechanics and flow simulation models do not account for such details; however, in this paper, we used in-house multi-phase dual-porosity model to show the effect and significance of compaction drive on production performance of a Bakken reservoir.
We have observed that the energy provided by the reservoir compaction increases cumulative fluid production, and numerical models that has constant compressibility values in their transport equations underestimates the cumulative production. Our modeling results indicate that the energy provided by the reservoir compaction increases cumulative fluid production by eleven percent for the Bakken example. Thus, using the results of this paper, we demonstrate the effect of rock elastic properties and stress-dependent porosity and permeability on reservoir long-term performance.