Production History Matching and Forecasting of Shale Assets Using Pattern Recognition

Generating long-term development plans and reservoir management of shale assets has continued apace. In this study, a novel method that integrates traditional reservoir engineering with pattern recognition capabilities of artificial intelligence and data mining is applied in order to accurately and efficiently model fluid flow in shale reservoirs. The methodology is efficient due to its relatively short development time and is accurate as a result of high quality history matches it achieves for individual wells in a multiwell asset. The technique that is named Artificial Intelligence (AI) Based Reservoir Modeling is a formalized and comprehensive, full-field empirical reservoir model. It integrates all aspects of shale reservoir development from well location and configuration to reservoir characteristics and to completion and hydraulic fracturing. This approach not only has a much faster turnaround time compared to the numerical simulation techniques, but also models the production from the field with good accuracy, incorporating all the available data. This integrated framework enables reservoir engineers to compare and contrast multiple scenarios and propose field development strategies. AI-based Modeling is applied to a Marcellus Shale asset that includes 135 horizontal wells from 43 pads with different landing targets. The full field AI-based Shale model is used for predicting the future well/reservoir performance, forecasting the behavior of new wells/pads and to assist in planning field development strategies. Furthermore, this study takes advantage of applying advanced pattern recognition tools in order to investigate the impact of design and native parameters on gas production as well as optimizing the completion and stimulation parameters for newly planned wells

Multiscale Modeling of Smart Materials under Static and Dynamic Thermo-mechanical Loading

Engineering material systems with tailored capabilities are a topic trend in plethora of research. Polymer based Artificial Muscle, PAM, and Shape memory polymer fiber, SMPF, enable structural engineers to incorporate smartness functionality into their design through programming cycles. Smartness functionality leads to the production of artificial muscles with different load carrying capability. SMPF is another category of smart materials, which are capable of being micro-structurally engineered to isolate vibration at different temperature and frequency conditions. The smartness functionality offers the adjustment between inherent properties of these materials with their industrial applications through modeling techniques. Mixture of phenomenological, numerical, mathematical models provides phenomenological Multiscale model to study effect of thermal fluctuation on mechanical response of polymer based artificial muscle. This model provides an insight to the nature of thermo mechanical response at macroscopic scale as well as the theory behind stress-strain evolution over working temperature. Multiscale modeling techniques is applied to study dynamic response through relating the damping and storage properties of a smart material, SMPF, to the stiffness and damping coefficient of a single degree of freedom system, SDOF. Damping coefficient, c, is related to the loss factor and natural frequency of the system; equivalent stiffness, k, is correlated to the storage modulus and geometry of the specimen

Effect of two different tooth bleaching techniques on microhardness of giomer

Tooth bleaching is a safe and conservative treatment modality to improve the esthetic appearance of discolored teeth. One of the problems with the use of bleaching agents is their possible effect on surface microhardness of resin-based materials. The present study was carried out to evaluate the effect of in-office and at-home bleaching on surface microhardness of giomer. Seventy-five disk-shaped giomer samples (Beautifil II) were prepared and cured with a light-curing unit. The samples were randomly assigned to three groups (n=25). In group 1 (control), the samples were stored in distilled water for 14 days. The samples in groups 2 and 3 underwent a bleaching procedure with 15% carbamide peroxide (CP) (8 hours daily) and 45% CP (30 minutes daily), respectively, for 14 days. Finally, the microhardness of samples was measured with Vickers hardness tester using a 100-g force for 20 seconds. One-way ANOVA was used to compare the mean microhardness values among the study groups, followed by post hoc Tukey test for two-by-two comparison of the groups. Statistical significance was set at P<0.05. One-way ANOVA showed significant differences in the mean microhardness values among the study groups (P<0.001). Based on the results of Tukey test, microhardness in the bleached groups was significantly less than that in the control group (P<0.0005). In addition, microhardness in the 45% CP group was significantly less than that in the 15% CP group (P<0.0005). Use of both bleaching agents during in-office and at-home bleaching techniques resulted in a decrease in surface microhardness of giomer. The unfavorable effect of in-office bleaching (45% CP) was greater than that of at-home bleaching (15% CP)

The Dynamics of Energy-Grain Prices with Open Interest

This paper examines the short- and long-run daily relationships for a grain-energy nexus that includes the prices of corn, crude oil, ethanol, gasoline, soybeans, and sugar, and their open interest. The empirical results demonstrate the presence of these relationships in this nexus, and underscore the importance of ethanol and soybeans in all these relationships. In particular, ethanol and be considered as a catalyst in this nexus because of its significance as a loading factor, a long-run error corrector and a short-run adjuster. Ethanol leads all commodities in the price discovery process in the long run. The negative cross-price open interest effects suggest that there is a money outflow from all commodities in response to increases in open interest positions in the corn futures markets, indicating that active arbitrage activity takes place in those markets. On the other hand, an increase in the soybean open interest contributes to fund inflows in the corn futures market and the other futures markets, leading to more speculative activities in these markets. In connection with open interest, the ethanol market fails because of its thin market. Finally, it is interesting to note that the long-run equilibrium (cointegrating relationship), speeds of adjustment and open interest across markets have strengthened significantly during the 2009-2011 economic recovery period, compared with the full and 2007-2009 Great Recession periods.Energy-grain price nexus, open interest, futures prices, ethanol, crude oil, gasoline, corn, soybean, sugar, arbitrage, speculation.

Role of China in Africa : three case studies of Angola, Ethiopia and Zambia

The purpose of this paper is to explore the implications of the rapid expansion of Chinese engagement in the African continent. This engagement is characterised by ‘trade, investment, foreign aid and government-sponsored bilateral cooperation’ which is highly varied, politicised, and wrought with scrutiny. It is important to stress that China’s role and its implications vary between countries due to differing socio-political, economic and environmental contexts. Therefore, this paper will focus on three examples of China’s development ‘partnerships’: Angola, Ethiopia and Zambia. Angola will be used to analyse China’s oil-backed infrastructure projects. Ethiopia will be explored to challenge allegations of China’s purely extractive agenda as it is a non-oil exporting developmental state which uses Chinese ‘assistance’ to overcome problems of poverty and food insecurity. Zambia will be used to discuss detriments of Chinese engagement. This paper concludes Chinese engagement in Africa to be multifaceted in light of her varied ‘development partnerships’, seen through the Angolan, Ethiopian and Zambian case studies. Sino-Ethiopian and Sino-Zambian relations particularly highlight the varied implications of Chinese engagement. In light of domestic and international backlash particularly regarding treatment of workers in Chinese owned factories in Zambia, the Chinese are working to sweeten perceptions with public health investment and campaigns. This illustrates dynamism in Chinese engagement which, this paper concludes, will sustain China’s role in Africa

A Block Coordinate Descent-based Projected Gradient Algorithm for Orthogonal Non-negative Matrix Factorization

This article utilizes the projected gradient method (PG) for a non-negative matrix factorization problem (NMF), where one or both matrix factors must have orthonormal columns or rows. We penalise the orthonormality constraints and apply the PG method via a block coordinate descent approach. This means that at a certain time one matrix factor is fixed and the other is updated by moving along the steepest descent direction computed from the penalised objective function and projecting onto the space of non-negative matrices. Our method is tested on two sets of synthetic data for various values of penalty parameters. The performance is compared to the well-known multiplicative update (MU) method from Ding (2006), and with a modified global convergent variant of the MU algorithm recently proposed by Mirzal (2014). We provide extensive numerical results coupled with appropriate visualizations, which demonstrate that our method is very competitive and usually outperforms the other two methods

King Huviška, Yima, and the Bird : observations on a paradisiacal state

This essay discuses the significance of the unique gold coin of the Kushan king, Huviška. The legend on the coin reads Imšao which recalls the ancient Indo-Iranian mythic figure, Yima/Yama. It is contended that the reason for which Yima/Yama is portrayed on the coin with a bird on his hand is not the idea of Glory and his reign, but rather the paradaisical state according to the Wīdēwdād, where Yima/Yama ruled over the world. It is contended that Huviška aimed at presenting himself in this manner to his subjects who were familiar with the Avestan and mythic Indo-Iranian lore

The Dynamics of Energy-Grain Prices with Open Interest

This paper examines the short- and long-run daily relationships for a grain-energy nexus that includes the prices of corn, crude oil, ethanol, gasoline, soybeans, and sugar, and their open interest. The empirical results demonstrate the presence of these relationships in this nexus, and underscore the importance of ethanol and soybeans in all these relationships. In particular, ethanol and be considered as a catalyst in this nexus because of its significance as a loading factor, a long-run error corrector and a short-run adjuster. Ethanol leads all commodities in the price discovery process in the long run. The negative cross-price open interest effects suggest that there is a money outflow from all commodities in response to increases in open interest positions in the corn futures markets, indicating that active arbitrage activity takes place in those markets. On the other hand, an increase in the soybean open interest contributes to fund inflows in the corn futures market and the other futures markets, leading to more speculative activities in these markets. In connection with open interest, the ethanol market fails because of its thin market. Finally, it is interesting to note that the long-run equilibrium (cointegrating relationship), speeds of adjustment and open interest across markets have strengthened significantly during the 2009-2011 economic recovery period, compared with the full and 2007-2009 Great Recession periods.Energy-grain price nexus; open interest; futures prices; ethanol; crude oil; gasoline; corn; soybean; sugar; arbitrage; speculation

Full Field Reservoir Modeling of Shale Assets Using Advanced Data-Driven Analytics

Hydrocarbon production from shale has attracted much attention in the recent years. When applied to this prolific and hydrocarbon rich resource plays, our understanding of the complexities of the flow mechanism (sorption process and flow behavior in complex fracture systems - induced or natural) leaves much to be desired. In this paper, we present and discuss a novel approach to modeling, history matching of hydrocarbon production from a Marcellus shale asset in southwestern Pennsylvania using advanced data mining, pattern recognition and machine learning technologies. In this new approach instead of imposing our understanding of the flow mechanism, the impact of multi-stage hydraulic fractures, and the production process on the reservoir model, we allow the production history, well log, completion and hydraulic fracturing data to guide our model and determine its behavior. The uniqueness of this technology is that it incorporates the so-called “hard data” directly into the reservoir model, so that the model can be used to optimize the hydraulic fracture process. The “hard data” refers to field measurements during the hydraulic fracturing process such as fluid and proppant type and amount, injection pressure and rate as well as proppant concentration. This novel approach contrasts with the current industry focus on the use of “soft data” (non-measured, interpretive data such as frac length, width, height and conductivity) in the reservoir models. The study focuses on a Marcellus shale asset that includes 135 wells with multiple pads, different landing targets, well length and reservoir properties. The full field history matching process was successfully completed using this data driven approach thus capturing the production behavior with acceptable accuracy for individual wells and for the entire asset

Association of the Low Pregnancy-Associated Plasma Protein A and Pregnancy Complications in the First Trimester: A Prospective Cohort Study

Background and Aims: This study aimed to assess the association between the low maternal serum of pregnancy-associated plasma protein-A (PAPP-A) during the first trimester and pregnancy outcomes. Materials and Methods: We conducted a prospective cohort study of 118 pregnant women undergoing first-trimester screening between 2016 and 2017 at Taleghani and Imam Hussein hospital in Tehran, Iran. We recorded demographic data and blood samples were to analyze the value of PAPP-A, based on which we divided the participants into two groups: PAPP-A&gt;10 percentile as a control group and PAPP-A≤10th percentile as a study group. The pregnancies underwent follow-up observations for obstetric complications during pregnancy. Chi-square or Fisher exact test and Mann-Whitney U test were applied to analyze data by SPSS 26. Results: In this study,118 pregnant women were enrolled. Our results show a significant association between low PAPP-A (&lt;10th percentile) and preterm labor, small for gestational age (SGA), hypertension, preeclampsia (P&lt;0.05), but no statistically significant difference was found between low PAPP-A and stillbirth. Demographic data, including age, gravida, parity, BMI, had no relationship with low PAPP-A, significantly (P&gt;0.05). Conclusion: Low PAPP-A is associated with adverse outcomes; thus, measuring the PAPP-A within the first trimester is suggested for timely management