bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimization of Immiscible Alkaline-Surfactant-Alternated-Gas/CO2 Flooding in an Upper Assam Oilfield
Authors: Phukan Ranjan , Borgohain Gogoi Subrata , Tiwari Pankaj , Singh Vadhan Ramanpreet 
Published: April 2019

Abstract
The immiscible alkaline-surfactant-alternated-gas/CO2 (ASAG) flooding comprise of alternately injecting alkali-surfactant (AS) formulations and gas/CO2 into the formation leading to the generation of in-situ foam. Optimization of the ASAG process is a necessary step prior to its application in a particular oil field for cost-effective enhanced oil recovery (EOR) approach. In this article, optimization of the ASAG flooding in an Upper Assam oilfield was studied through lab-scale core flood experiments. First, reservoir core samples, crude oil, and formation water of the oilfield were characterized. Second, AS formulations were designed through foam stability tests, phase behavior tests, and equilibrium interfacial tension (IFT) measurements. Third, a total of 15 lab-scale core flooding tests were conducted through different ASAG injection schemes. Crude oil of the oilfield was medium gravity with acid number equaling 2.1 mg of KOH/gm. The average porosity and permeability of core samples were found to be approximately 20% and 6 md respectively. XRD and FESEM analysis showed the presence of silica, quartz and smectite clays in the rock samples. The total salinity based on the ionic composition of the formation water was found to be 3458 ppm. The foam stability tests indicated that maximum foaming behavior in presence of crude oil occurred at specific combination of surfactant, alkali, and brine salinity. The optimal salinities determined by salinity scan were found to concur with the salinities at which lowest equilibrium IFT values were obtained for the AS formulations. The lowest IFT values were in the ultra-low range (less than 0.01 mN/m). The favorable operating parameters with best oil recovery during ASAG flooding were obtained with 1:1 slug ratio, 0.25 pore volume (PV) slug size, 0.2 ml/min gas injection rate, 2 PV injected fluid volume and combined gas-liquid tapering. The application of negative salinity gradient during ASAG flooding could further improve the additional oil recovery to 29.19 % original oil in place. Moreover, the use of a sacrificial agent during waterflooding proved to be valuable in economically improving the cumulative oil recovery by ASAG flooding.
Innovative Convolutional Neural Networks Applied in Dynamometer Cards Generation
Authors: Peng Yi , Zhao Ruidong , Zhang Xishun , Shi Junfeng , Chen Shiwen , Gan Qingming , Li Gangyao , Zhen Xiaoxiong , Han Tao 
Published: April 2019

Abstract
There are nearly 200,000 oil wells producing in PetroChina and 94% are sucker-rod pumping wells. Dynamometer card is essential for well diagnosis and optimization, which cost huge investment on sensor installations and maintenance. This paper present an innovative artificial intelligence method to get the card data directly from electrical parameters.
With the development of artificial intelligent technology, many challenges in oil and gas industry have the potential to be addressed by developing artificial intelligent and Big Data analytic tools. This paper presents a method to get the dynamometer card using the electrical data collected during production. In our method, we start with building our training datasets consist of dynamometer card and corresponding electrical data with various well performance conditions. We using the state of art artificial intelligent deep learning algorithm to training the data and make a general model to generate the dynamometer card from electrical data.
This method is based on image processing and deep learning, which eliminates the disadvantage of conventional physical-mechanism based model. After tested training datasets in several oilfields in PetroChina, the model demonstrates very high accuracy with almost 90% similarity to corresponding dynamometer card. The calculated dynamometer card from our model are good consistent with the field data too. In addition, this method has been testing on hundreds of wells newly produced well utilizing sucker-rod pumping system, which would reduce millions of investment on the sensors and equipment on average 5000 newly producing wells in PetroChina each year.
As a result, with the fast development of artificial intelligence technology and expanding training datasets, the deep learning method will be fully utilized in more sucker-rod pump wells to generate the dynamometer card, which is also part of the intelligent well strategy. In the low oil price trend, artificial intelligence is a good choice to lower the investment and maintenance cost for mature fields and newly produced fields.
Enhancing Reservoir Management Quality and Efficiency of Thermal Assets with Data-Driven Models
Authors: Kim Tae Hyung 
Published: April 2019

Abstract
Temperature monitoring is the most important surveillance in thermal assets, but temperature logging is limited in frequencies and locations. In addition, it is extremely difficult to review all the measured temperature and injection data manually since there are 10,000+ wells in Kern River field. To overcome the limitations, data-driven reservoir temperature models are presented that are built using past temperature logs and steam injection rates of the Kern River field, California.
Based on the physics and geologic understanding the reservoir, adequate input features were selected and queried. Data cleanup was conducted to remove erroneous data or fix data errors using statistical tools such as multivariate Gaussian distribution. Voronoi diagram based dynamic injector selection algorithm (DISA) was developed to correctly capture the injectors which impact on temperature changes of a temperature observation well. Based on geologic characteristics of the Kern River, reservoir was divided into two sub-reservoirs, North-East and South-West. Two full field models were developed for predicting maximum and mean temperatures of a heated zone with multi-layer perceptron for both sub-reservoirs using about 120,000 data points from over 25,000 temperature curves measured at 700+ temperature observation wells.
To estimate proper model update frequencies and verify the process, three yearly models (models 2015, 2016, and 2017) were built and validated by using one-year future temperature predictions in 2016, 2017, and 2018. For instance, model 2015 was trained with data until the end of 2015 and validated against 2016 data. Maximum temperature prediction r2 of 2017 South-West and North-East models were 0.96 and 0.98, respectively. Model 2017 has been deployed for alerting exception cases automatically and flagging abnormal temperature measurements. Also, the models improve the quality of heat injection design by providing temperature predictions based on planned heat injection rates. This novel automated workflow with data-driven models enhances reservoir management efficiency by reducing engineers’ unproductive time such as data manipulation and allowing them to focus on value-added works like analysis and optimization.
High Resolution Dynamic Drainage Height Estimations using Subsurface DNA Diagnostics
Authors: Percak-Dennett Elizabeth , Liu Joanne , Shojaei Hasan , Luke Ursell , Thomas Ishoey 
Published: April 2019

Abstract
Subsurface DNA Diagnostics™ is a low-risk, high-resolution evaluation tool offering oil and gas operators a measurement of fluid movement in the subsurface. DNA sequencing methodologies that use subsurface DNA markers acquired from well cuttings and produced fluids are currently used in all major US unconventional basins to elucidate drainage heights for new and existing wells. Dynamic drainage height estimations are especially important in field-wide development, when actionable turnaround time of drainage height estimates is a priority to improve subsurface understanding and maximize reservoir economics.
For this work, well cuttings were collected during drilling every 10’ MD from a 1000’ vertical section of interest in the Permian's highly stratified Wolfcamp formation. Subsurface DNA was extracted and sequenced from cuttings at the sub-formation level to create a robust DNA marker profile. Produced fluids were collected every 2 weeks through 140 days. DNA markers from each sub-formation were compared to every fluid time point using a Bayesian mixture modeling algorithm. This method produces an estimate of the relative contribution of DNA markers from each sub-formation for each fluid sample over time.
The result of Subsurface DNA Diagnostics is a novel subsurface log that characterizes DNA markers as a function of depth, and identifies the intervals with relative higher productivity compared to the other intervals over time. These drainage metrics are then overlaid with existing petrophysical properties to refine knowledge of oil and water behavior within different sub formations during a well's lifetime.
Subsurface DNA Diagnostic's use of well cuttings and produced fluids enables rapid scalability due to low-risk sampling (no downhole tools or lost production time) and minimal field personnel time. As such, an operator can relatively quickly characterize the drainage height of their field over time using several vertical baselines and a routine sampling protocol of producing wells in both exploratory, development, and producing phases of the asset.
Constrained Least-Squares Multiwell Deconvolution
Authors: Cumming J , Jaffrezic V , Whittle T , Gringarten A C 
Published: April 2019

Abstract

Objectives/Scoper
In this paper, we reduce non-uniqueness and ensure physically feasible results in multiwell deconvolution by incorporating constraints and knowledge to the methodology of Cumming et al. (2014). The constraints discourage non-physical shapes for the deconvolved derivatives and improve solution quality. We also encode knowledge on the reservoir, for instance, if it is a closed system, we impose that all deconvolved derivatives should tend towards a common unit slope at some future time.

Methods, Procedures, Process
Single-well deconvolution (von Schroeter, Hollaender and Gringarten, 2001, 2004) transforms variable-rate pressure data from a well test, into a single constant-rate drawdown with the same duration as the pressure history. The resulting deconvolved derivative shows features not visible within the durations of any of the test build up or drawdown periods and therefore enhance analysis and interpretation. Single-well deconvolution, however, is applicable only to individual isolated wells. Multiwell deconvolution - introduced by Levitan (2006) and developed by Cumming et al. (2013) - generalises the solution methodology to the problem of multiple wells within the same connected system.

Results, Observations, Conclusions
Multiwell deconvolution yields not only the constant-rate deconvolved derivatives for every well in the system, but also the interference effects from any one well to any other. These interference effects provide valuable information concerning storativity and connectivity within the reservoir, which is not accessible otherwise. Multiwell deconvolution is, however, an overdetermined problem and may therefore yield non-unique solutions that can reproduce pressure histories to a similar degree of precision, but correspond to different, potentially conflicting, and possibly non-physical responses and interpretations. This non-uniqueness and the possibility of non-physical solutions need to be reduced for practical multiwell deconvolution of field data.

Novel/Additive Information
We demonstrate how a combination of constraints on the shapes of the deconvolved derivatives and knowledge of the reservoir results in an improved level of quality and consistency in multiwell deconvolution solutions. We illustrate this new constrained least-squares multiwell deconvolution approach with synthetic examples with known solutions involving up to nine wells.
Application of Machine Learning in Transient Surveillance in a Deep-Water Oil Field
Authors: Chang Oliver , Pan Yan , Dastan Aysegul , Teague David , Descant Frank 
Published: April 2019

Abstract
There are on-going efforts in digital transformation in different aspects of hydrocarbon recovery. For well performance surveillance, we have developed the key elements of a Transient Data Surveillance Machine to efficiently process and analyze all transient data from continuous measurements at the wells, allowing for full utilization of the available data. The workflow has been applied at wells in a deep-water oil field in Gulf of Mexico and proved to be effective.
We developed Machine Learning (ML) algorithms and techniques to efficiently process and analyze pressure-rate transient data. Following the automatic workflow, K-mean clustering is used to identify shut-in periods, maximum-slope method is used to synchronize pressure and rate data, Supported Vector Machine algorithm combined with Kernel method is used for transient flow-regime recognition, followed by Non-Linear Regression using physical models to estimate reservoir and well properties and assess uncertainty.
Through synthetic case and field data testing, we demonstrated that the ML method is tolerant to data noise. Even at 15% of noise level, which is much higher than standard pressure gauge data, the successful rate is 98% in flow-regime identification. However, it is sensitive to data outliers, and we need to include other techniques, such as wavelet data processing, in the workflow. Adding real field data with associated reservoir models that are validated by experts into the training data set could increase the accuracy of pattern recognition 10% more than training with only analytical solutions. The application of our workflow in a deep-water oil field in Gulf of Mexico, which started oil production in 2009 with all wells with permanent downhole pressure gauges, helped to process and analyze transient data from shut-in’s (70% planned transient tests and 30% operation related) efficiently, and derived information about well productivity changes, interference among wells, and permeability reduction due to rock compaction. This enabled continuous well monitoring and effective identification of well productivity issues.
The novelty of our Transient Data Surveillance Machine is its capacity in handling huge amounts of dynamic data and its efficiency using real-time data diagnosis for operation decisions and reservoir management.
Pressure Transient Analysis in Fractured Horizontal Wells with Fracture Networks
Authors: Chen Z. , Liao X. , Liu Q. , Wang L. , Wang S. , Tang X. , Zhang J. 
Published: April 2019

Abstract
Drilling fractured horizontal well is now a common practice to improve the productivity of unconventional wells. With the reactivated natural fractures, the technique of fractured horizontal wells can generate a large amount of complex fracture networks of hydraulic fractures (HF) and micro fractures (MF) in unconventional reservoirs. In this paper, an efficient semi-analytical model is developed for pressure transient analysis in horizontal wells by considering hydraulic fracture networks as well as natural fracture networks. During the model development, we develop the diffusivity equation for fluid flow in formation matrix using line source function. With the nodal analysis technique, the flow interplay at fracture intersections is eliminated and the diffusivity equations for fluid flow in hydraulic fractures are built. The pressure transient solution of these diffusivity equations is obtained by using Laplace transforms and Stehfest numerical inversion (Stehfest, 1970).
The results show that different from a single "dip"––the classical dual-porosity feature of naturally fractured reservoirs ––in the pressure derivative, the reservoir system exhibits many other different pressure behaviors like "fluid feed", "pseudo-boundary dominated flow", etc. All these pressure behaviors are associated with the properties and geometries of natural/hydraulic fractures. What's more, the pressure response for fracture network horizontal wells with natural fracture networks can be divided into some flow regimes, which include: (1) the first bilinear flow, (2) "MF-HF" support, (3) the second bilinear flow, (4) formation linear flow, (5) cross flow, and (6) pseudo-radial flow. Model reliability is demonstrated by a numerical verification. The efficient semi-analytical model in this work can substantially reduce the computational burdens of numerical simulators for transient pressure analysis in shale reservoirs with hydraulic and/or natural fracture networks.
Multiphase Permeability Evolution in Low Permeability Sandstones from Surfactant-Treated Fractring Fluids
Authors: Abaa Kelvin , Yilin-Wang John , Elsworth Derek , Ityokumbul Mku 
Published: April 2019

Abstract
Improper selection and design of surfactant treatments intended to remove damage aqueous phase trapping often ends up causing other types of formation damage. This is due to our limited understanding of the processes that govern rock-fluid and fluid-fluid interactions between surfactants, fracturing fluid and the formation during invasion and flowback of the injected fluids in the rock matrix. This study focuses on the laboratory investigation of the processes governing multiphase permeability evolution during invasion of fracturing fluids treated with surfactants in low permeability sandstones.
Two surfactant chemicals, Triton X-100, a hydrocarbon surfactant and Novec FC-4430, a fluorosurfactant, were used to treat filtrate from slickwater, linear gel and borate crosslinked gel fluids. Multiphase experiments were conducted on sandstones cores flooded with the treated fluids. The experiments consist of steady state gas displacements and pulse decay permeability measurements. The obtained data include gas flow rate, pore volumes of liquid expelled and gas relative permeability curves.
Experimental results indicate that treatments with fluorosurfactant improved liquid and gas permeability recovery for all fracturing fluids. Additionally, maximum liquid and gas permeability recovery was achieved when the core was pretreated with fluorosurfactant. Our results show that multiphase permeability evolution with surfactant treatment is driven by wettability alterations rather than reduction in interfacial tension.
Multiphase permeability data could be used in modeling of post fracture well performance and formation damage assessment in low permeability sandstones. The new findings will serve as a guide for optimizing fracturing fluid/surfactant treatment in tight gas reservoirs.
Slickwater, Hybrid, Self-Suspending Proppant: Practical Use of Data Analytics to Determine Hydraulic Fracture Fluids
Authors: Sedillos Lia , Saxena Jayant , Convers Carlos , Dean Elio , Roundtree Russell 
Published: April 2019

Abstract
Utilizing fluids that have friction reducing qualities and/or viscosity building properties are desirable for hydraulic fracturing in multi-stage horizontal wells. Horizontal wells targeting the Middle Bakken used various fluid types for stimulation were analyzed in this study. This paper uses data analytics to show that the answer to "which fluid type is better?". By applying statistical methods of k-means clustering, t-tests as well as multivariate analysis, a more robust answer was obtained in the comparison between slickwater, crosslinked gel, linear gel, hybrid systems, and Self-Suspending proppant. The first part of the analysis compared independent variables (lateral length, true vertical depth (TVD), liquid loading, sand loading, and fluid type) to well productivity in 532 Middle Bakken wells. Multivariate analysis was conducted to determine the dominant variables. Based on those, the data were filtered in two different ways: first on sand loading and secondly on lateral length and TVD.
The second part of the analysis applied distinct k-means clustering. A multivariate model was built to accommodate the influence of each variable in each cluster. Having found inconclusive results, ANOVA (Analysis of Variance) analysis was conducted to analyze which fluid type resulted in the best overall well production. Several conclusions are demonstrated in this paper. First, industry stimulation treatment data are rarely crafted with thorough Design of Experiment rigor. This challenges many of the assumptions in statistics and data analytics to provide for unbiased analysis. Second, while lateral length, TVD and total sand are all seemingly independent variables physically, they were found to have a certain degree of statistical collinearity; a measure of variable dependence. Third, it was shown that wells using slickwater and 1Self-Suspending Proppant outperform wells using other proppant types in terms of overall production in the Middle Bakken. Hence, while the perfect frac job still eludes us, establishing a framework of unbiased analysis provide us with a robust approach to answer one of the key questions of our day: which fluid type is better? The novel thing about this type of analysis is that it takes thousands of different types of data points and narrows down what the most important variables in the data are. In an age where data is king, this approach follows statistic principles designed for handling such data.
Prescriptive Analytics for Completion Optimization in Unconventional Resources
Authors: Gharib Shirangi Mehrdad , Oruganti Yagna , Wilson Thomas , Furlong Edward , Winter Eric , Martin Joel , Yancy Robert 
Published: April 2019

Abstract
In this work, we present and apply a comprehensive prescriptive analytics framework for well completion optimization in unconventional resources. The methodology involves 1) data processing, ingestion into databases, and data cleansing 2) application of AutoML for generating an accurate machine learning model, and 3) numerical optimization of decision parameters for minimizing an economic objective. Input parameters to the machine learning model include system parameters (such as well location and trajectory, existence and type of artificial lift) and decision parameters (such as number of stages, amount of stimulation material). The AutoML process automatically searches among various machine-learning algorithms (e.g., neural networks, random forest), to find the best algorithm and the best associated hyper-parameters. In the third step, a multi-objective optimization process is implemented to simultaneously optimize the 12-month oil producton (Qoil) and the completion cost divided by Qoil. The methodology is applied to a real case in the Permian Basin, in collaboration with Diamondback Energy. The results demonstrate that the two objectives are conflicting. The construction of the Pareto front, which shows a set of optimal solutions, provides a visual trade-off for decision makers and enables them to select a strategy that minimizes the cost, but does not sacrifice too much of the 12-month oil production.