A New Approach for Predrilling the Unconfined Rock Compressive Strength Prediction

Abstract

A reasonable knowledge of rock's physical and mechanical properties could save the cost of drilling and production of a reservoir to a large extent by selection of proper operating parameters. In addition, a master development plan (MDP) for each oilfield may contain many enhanced oil recovery procedures that take advantage of rock mechanical data and principles. Thus, an integrated rock mechanical study can be considered an investment in field development.

The unconfined compressive strength (UCS) of rocks is the important rock mechanical parameter and plays a crucial role when drilling an oil or gas well. A drilling operation is an interaction between the rock and the bit and the rock will fail when the resultant stress is greater than the rock strength. UCS is actually the stress level at which rock is broken down when it is under a uniaxial stress. It can be used for bit selection, real-time wellbore stability analysis, estimation an optimized time for pulling up the bit, design of enhanced oil recovery (EOR) procedures, and reservoir subsidence studies.

Rock strength can be estimated along a drilled wellbore using different approaches, including laboratory tests, core–log relationships, and penetration model approaches. Although this rock strength profile can be used for future investigation of formations around the wellbore, they are actually dead information. Dead rock strength data may not be useful for designing a well in a blind location (infill drilling). Rock strength should be predicted prior to drilling operations. These sort of data are helpful in proposing a drilling program for a new well.

In this research, new equations for estimation of rock strength in Ahwaz oilfield are formulated based on statistical analysis. Then, they are utilized for estimation of the rock strength profile of 36 wells in a Middle Eastern oilfield. An artificial neural network is then utilized for prediction of UCS in any predefined well trajectory. Cross-validation tests showed that the results of the network were compatible with reality. This approach has proven to be useful for estimation of any designed well trajectory prior to drilling.     

Batch and fixed‐bed column adsorption of Cu(II), Pb(II) and Cd(II) from aqueous solution onto functionalised SBA‐15 mesoporous silica

Functionalised SBA‐15 mesoporous silica with polyamidoamine groups (PAMAM‐SBA‐15) was successfully prepared with the structure characterised by X‐ray diffraction, nitrogen adsorption–desorption, Fourier transform infrared spectra and thermogravimetric analysis. PAMAM‐SBA‐15 was applied as adsorbent for Cu(II), Pb(II) and Cd(II) ions removal from aqueous solution. The effects of the solution pH, adsorbent dosage and metal ion concentration were studied under the batch mode. The Langmuir model was fitted favourably to the experimental data. The maximum sorptive capacities were determined to be 1.74 mmol g^?1 for Cu(II), 1.16 mmol g^?1 for Pb(II) and 0.97 mmol g^?1 for Cd(II). The overall sorption process was fast and its kinetics was fitted well to a pseudo‐first‐order kinetic model. The mean free energy of sorption, calculated from the Dubinin–Radushkevich isotherm, indicated that the sorption of lead and copper, with E > 16 kJ mol^?1, followed the sorption mechanism by particle diffusion. The adsorbent could be regenerated three times without significant varying its sorption capacity. A series of column tests were performed to determine the breakthrough curves with varying bed heights and flow rates. The breakthrough data gave a good fit to the Thomas model. Maximum sorption capacity of 1.6, 1.3 and 1.0 mmol g^?1 were found for Cu(II), Pb(II) and Cd(II), respectively, at flow rate of 0.4 mL min^?1 and bed height of 8 cm, which corresponds to 83%, 75% and 73% of metallic ion removal, respectively, which very close to the value determined in the batch process. Bed depth service time model could describe the breakthrough data from the column experiments properly. © 2012 Canadian Society for Chemical Engineering     

A new method for detecting various variants of GoF design patterns using conceptual signatures

Software design patterns are well-known solutions for solving commonly occurring problems in software design. Detecting design patterns used in the code can help to understand the structure and behavior of the software, evaluate the quality of the software, and trace important design decisions. To develop and maintain a software system, we need sufficient knowledge of design decisions and software implementation processes. However, the acquisition of knowledge related to design patterns used in complex software systems is a challenging, time-consuming, and costly task. Therefore, using a suitable method to detect the design patterns used in the code reduces software development and maintenance costs. In this paper, we proposed a new method based on conceptual signatures to improve the accuracy of design pattern detection. So we used the conceptual signatures based on the purpose of patterns to detect the patterns’ instances that conform to the standard structure of patterns, and cover more instances of patterns’ variants and implementation versions of the patterns and improve the accuracy of pattern detection. The proposed method is a specific process in two main phases. In the first phase, the conceptual signature and detection formula for each pattern is determined manually. Then in the second phase, each pattern in the code is detected in a semi-automatic process using the conceptual signature and pattern detection formula. To implement the proposed method, we focused on GoF design patterns and their variants. We evaluated the accuracy of our proposed method on five open-source projects, namely, Junit v3.7, JHotDraw v5.1, QuickUML 2001, JRefactory v2.6.24, and MapperXML v1.9.7. Also, we performed our experiments on a set of source codes containing the instances of GoF design patterns’ variants for a comprehensive and fair evaluation. The evaluation results indicate that the proposed method has improved the accuracy of design pattern detection in the code.

     

Application of Amine Modified Magnetic Nanoparticles as an Efficient and Reusable Nanofluid for Removal of Ba2+ in High Saline Waters

Silicon - This study describes an investigation on the application of functionalized nanoparticles used as a sorbent for extraction and removal of barium ions from high saline waters. Magnetic...     

β-Amyloid Targeting with Two-Dimensional Covalent Organic Frameworks: Multi-Scale In-Silico Dissection of Nano-Biointerface

Cytotoxic aggregation of misfolded β-amyloid (Aβ) proteins is the main culprit suspected to be behind the development of Alzheimer's disease (AD). In this study, Aβ interactions with the novel two-dimensional (2D) covalent organic frameworks (COFs) as therapeutic options for avoiding β-amyloid aggregation have been investigated. The results from multi-scale atomistic simulations suggest that amine-functionalized COFs with a large surface area (more than 1000 m²/gr) have the potential to prevent Aβ aggregation. Gibb's free energy analysis confirmed that COFs could prevent protofibril self-assembly in addition to inhibiting β-amyloid aggregation. Additionally, it was observed that the amine functional group and high contact area could improve the inhibitory effect of COFs on Aβ aggregation and enhance the diffusivity of COFs through the blood-brain barrier (BBB). In addition, microsecond coarse-grained (CG) simulations with three hundred amyloids reveal that the presence of COFs creates instability in the structure of amyloids and consequently prevents the fibrillation. These results suggest promising applications of engineered COFs in the treatment of AD and provide a new perspective on future experimental research.     

Measurement matrix design for CS-MIMO radar using multi-objective optimization

In this paper, we design a measurement matrix for a compressive sensing-multiple-input multiple-output radar in the presence of clutter and interference. To optimize the measurement matrix, three main criteria are considered simultaneously to improve detection and sparse recovery performance while suppressing clutter and interference. To this end, we consider three well-known criteria including Bhattacharyya distance, mutual coherency of sensing matrix, and signal-to-clutter-plus-interference ratio. Due to the use of simultaneous multi-objective functions, a multi-objective optimization (MOO) framework is exploited. Some numerical examples are provided to illustrate the achieved improvement of our proposed method in target detection and sparse recovery performance. Simulation results show that the proposed MOO technique for measurement matrix design can achieve superior performance in target detection compared with Gaussian random measurement matrix technique.     

Acetalated Dextran Nanoparticles Loaded into an Injectable Alginate Cryogel for Combined Chemotherapy and Cancer Vaccination

Vaccination represents a promising strategy for cancer therapy due to its ability to efficiently eliminate tumors from the host body and prevent their recurrence. Nevertheless, the current vaccines are still lacking efficacy. Combination therapies, such as those integrating chemotherapy with immunotherapy, represent a powerful tool to potentially circumvent this drawback. Herein, injectable alginate cryogels loaded with granulocyte‐macrophage colony‐stimulating factor and cytosine‐phosphodiester‐guanine‐rich oligonucleotides, are combined with spermine‐modified acetalated dextran nanoparticles (Sp‐AcDEX NPs), loaded with p53 activator Nutlin‐3a (Nut‐3a) for combined chemoimmunotherapy. The Sp‐AcDEX NPs are successfully loaded into the alginate cryogels and released over time. Furthermore, the delivery of the NPs from the cryogel enhances their accumulation in tumor tissue following peritumoral injection. Nut‐3a exerts toxicity towards the tumor cells and induces immunogenic cell death through the upregulation of surface calreticulin expression. Overall, this combination is a promising strategy to reduce cancer cell proliferation, induce immunogenic cell death, and accumulate drug payloads into the tumor. This approach may avoid cancer recurrence through the induction of in situ cancer vaccination mediated by antigens and danger signals released from the apoptotic cancer cells.