Fracturing Height Growth Restriction Technique Successfully Extended into Horizontal Wells in Ostracod Formation

The Ostracod formation in the Awali brownfield is an extremely challenging layer to develop because the tight carbonate rock is interbedded with shaly streaks and because of the presence of a nearby water-bearing zone. Although the Ostracod formation has been in development since 1960, oil recovery has not yet reached 5% because past stimulation attempts experienced rapid production decline. The current project incorporated aggressive fracture design coupled with a unique height growth control (HGC) workflow, improving the development of Ostracod reserves. The HGC technology is a combination of an engineering workflow supported by geomechanical modeling and an advanced simulator of in-situ kinetics and materials transport to model the placement of a customized, impermeable mixture of particles that will restrict fracture growth. The optimized treatment design included injections of the HGC mixture prior to the main fracturing treatment. This injection was done with a nonviscous fluid to improve settling to create an artificial barrier. After the success of a trial campaign in vertical wells, the technique was adjusted for the horizontal wellbores. The high clay content within the Ostracod layers creates a significant challenge for successful stimulation. The high clay content prevents successful acid fracturing and leads to severe embedment with conventional proppant fracturing designs. We introduced a new approach to stimulate this formation with an aggressive tip-screenout design incorporating a large volume of 12/20-mesh proppant to obtain greater fracture width and conductivity, resulting in a significant and sustained oil production gain. The carefully designed HGC technique was efficient in avoiding fracture breakthrough into the nearby water zone, enabling treatments of up to 450,000 lbm to be successfully contained above a 20-ft-thick shaly barrier with small horizontal stress contrast. Independent measurements proved that the fracture height was successfully contained. This trial campaign in vertical wells proved that the combination of aggressive, large fracture designs with the HGC method could help unlock the Ostracod’s potential. Three horizontal wells were drilled and simulated, each with four stages of adjusted HGC technique to verify if this aggressive method was applicable to challenging sand admittance in case of transverse fractures. This rare implementation of HGC mixtures in horizontal wells showed operational success and proof of fracture containment based on pressure signatures and production monitoring. The applied HGC technique was modified with additional injections and improved by advanced modeling that only recently became available. These contributed to a significant increase of treatment volume, making the jobs placed in the Ostracod some of the world’s largest utilizing HGC techniques. The experience gained in this project can be of a paramount value to any project dealing with hydraulic fracturing near a water formation with insufficient or uncertain stress barriers.

Chiral Aminoalcohols and Squaric Acid Amides as Ligands for Asymmetric Borane Reduction of Ketones: Insight to In Situ Formed Catalytic System by DOSY and Multinuclear NMR Experiments

A series of squaric acid amides (synthesized in 66–99% isolated yields) and a set of chiral aminoalcohols were comparatively studied as ligands in a model reaction of reduction of α-chloroacetophenone with BH3•SMe2. In all cases, the aminoalcohols demonstrated better efficiency (up to 94% ee), while only poor asymmetric induction was achieved with the corresponding squaramides. A mechanistic insight on the in situ formation and stability at room temperature of intermediates generated from ligands and borane as possible precursors of the oxazaborolidine-based catalytic system has been obtained by 1H DOSY and multinuclear 1D and 2D (1H, 10/11B, 13C, 15N) NMR spectroscopy of equimolar mixtures of borane and selected ligands. These results contribute to better understanding the complexity of the processes occurring in the reaction mixture prior to the possible oxazaborolidine formation, which play a crucial role on the degree of enantioselectivity achieved in the borane reduction of α-chloroacetophenone.

Bayesian Hierarchical Growth Model for Experimental Data on the Effectiveness of an Incentive-Based Weight Reduction Method

The objective of this current research is to model the experimental data on the effectiveness of an incentive-based weight reduction method by using Bayesian hierarchical growth models. Three Bayesian hierarchical growth models are proposed, namely parametric Bayesian hierarchical growth model with correlated intercept and slope random effects model, parametric Bayesian hierarchical growth model with no correlated intercept and slope random effects model and semi-parametric Bayesian hierarchical growth model with Dirichlet process mixture prior model. The data is obtained from forty eight (48) students who had participated in an experiment on weight reduction method. The students were divided equally into two groups: single and pair groups. The experiment was carried out over the period of three months with a weight reading session for every two weeks. At the end of the study, we had six repeated measures of each student’s weight in kg and some measures of covariates and factors. Our results showed that the best model for the above data based on the Bayesian fit indexes and the models’ flexibility is the semi-parametric Bayesian hierarchical growth model with Dirichlet process mixture prior model. The results of the semi-parametric model showed that the ‘growth’ or reduction rates of the weight reduction experiment relate to the students’ gender, height in cm, experimental group (single or pair) and time in term of weeks.

Many Mixture Components, Oh My: Extending the Spike and Slab to Bayesian Hypothesis Testing with Multinoulli Indicators

Bayesian analysis has become increasingly popular in the social-behavioral sciences. Because hypothesis testing has an important place on the mantel of psychological inquiry, an active area of research has been developing Bayesian analogs for commonly used frequentist tests.However, a major hurdle to this endeavour is computing the necessary ingredients, that is the marginal likelihood, resulting in the use of inflexible analytic solutions or approaches that avoid its computation altogether. In a similar spirit, I extend the spike and slab model, widely considered the gold standard for variable selection, to allow for flexible hypothesis testing. This is accomplished by employing multinoulli indicator variables, as opposed to Bernoulli, which results in a general solution for testing any number of hypotheses that correspond to components of a mixture prior distribution. In a motivating example, I first describe the qualitative relation of the proposed methodology to a popular Bayesian $t$-test, including extensions for one-sided and interval hypothesis tests. With the foundation laid, I proceed to a more complex example wherein themultinoulli spike and slab is used to model a correlation matrix, with the goal of testing joint hypotheses. This example investigated the associations among experimental effects from three cognitive inhibition tasks ($N = 121$), where the theoretical expectation is that they will be positively correlated. To the contrary, the results revealed that the null model of no associations better predicted the observed data than the positive effects model.The important topics of model selection and Bayesian model averaging are also discussed. I end with ideas to further extend the multinoulli spike and slab model. In addition, detailed {\tt R} code is provided that can serve as the building block for developing custom Bayesian models.

Model-based Trajectory Inference for Single-Cell RNA Sequencing Using Deep Learning with a Mixture Prior

Trajectory inference methods analyze thousands of cells from single-cell sequencing technologies and computationally infer their developmental trajectories. Though many tools have been developed for trajectory inference, most of them lack a coherent statistical model and reliable uncertainty quantification. In this paper, we present VITAE, a probabilistic method combining a latent hierarchical mixture model with variational autoencoders to infer trajectories from posterior approximations. VITAE is computationally scalable and can adjust for confounding covariates to integrate multiple datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. We also apply VITAE to jointly analyze two single-cell RNA sequencing datasets on mouse neocortex. Our results suggest that VITAE can successfully uncover a shared developmental trajectory of the projection neurons and reliably order cells from both datasets along the inferred trajectory.

Effect of Pre-Treatment with Sodium Chloride/Sulfuric Acid on the Bornite Concentrate Leaching in Chloride Medium

In this work, the effect of the pre-treatment of bornite concentrate with a sodium chloride–sulfuric acid mixture prior to leaching with chloride solutions was evaluated. The influence of the dosage of NaCl and the resting time in the copper solution was evaluated. The solid residues of the pre-treatment were characterized by XRD to evaluate the changes that took place in the bornite. The experimental results show that as the sitting time and the amount of sodium chloride added are increased, the dissolution of the sulfide species also increased. The pre-treatment affects Cu preferentially because it has greater mobility than iron in the bornite crystal lattice. The pre-treatment promotes the formation of soluble Cu and Fe species. The efficiency of the pre-treatment depends largely on the formation of hydrochloric acid in the mixture at the particle level, due to the ease with which it diffuses through the bornite particles, achieving greater penetration of the pre-treatment.