Integration of seismic attributes and production data for infill drilling strategies — A virtual intelligence approach

Field development strategies are at the forefront of common engineering practices in the oil and gas industry. Reservoir simulation is the most commonly applied methodology to generate an optimum field development plan. However, reservoir simulation can be an energy and cost intensive method that often relies on rather subjective assumption of input parameters, due to lack of accurate field data. In this paper, a new approach using Artificial Neural Network (ANN) technology is proposed to predict individual well performances and accordingly develop infill drilling strategies. Due to its predictive capabilities, ANN is used as a tool to construct a correlation for production prediction. Seismic attributes, which capture heterogeneity of the reservoir geology, and completion information are used as network inputs. In calculating the interference effects, the geometry of the flow system under consideration was used together with the geometric location and the starting production schedule of each well within the system. The method was successfully implemented on a case study of the 19N 94W Township of the Wamsutter field in Wyoming using actual seismic attributes, completion information, well configuration, and production data. Production predictions were generated by the network for all locations at which seismic attributes were available. More promising locations were then selected for infill drilling purposes based on predicted productions at these locations. The predicted initial rate and 10-yr cumulative production were considered in the selection of infill drilling locations with high productivity potential. Results from this work show that the ANN was able to map the relationship between production, completion information, interference effects, and reservoir characteristics captured in seismic attributes. The proposed methodology allowed the construction of spatial maps of gas production, revealing new sweet spots which could not be identified from the existing production history alone. The production maps derived from the ANN predictions contain important heterogeneous features associated with reservoir properties reflected in seismic data. Even though well interference was initially thought to have a limited effect on well performance for the case study presented, the incorporation of well interference parameters in the network design improved production predictions, suggesting that well interference has a more significant impact on well performance than originally anticipated.     

Silver and proton driven fluorescent multiple-mode molecular logic gates employing phthalocyanines

Spectral properties and switch behavior of two alkyl thia units bearing Zn (II) phthalocyanine derivatives (Pc-A and Pc-B) were investigated in solvents and in solid matrix of ethyl cellulose by means of absorption and emission spectroscopy. Fluorescence lifetime and fluorescence quantum yield values of the Zn (II) phthalocyanines were calculated. The employed phthalocyanines demonstrated multiple molecular logic gate functions operated by H⁺ and Ag⁺ ions as chemical inputs. The silver driven fluorescence modulation of the Pc-A arises from reversible variations in emission signal intensity at 717 nm. The Pc-B exhibited a similar decreasing emission response to proton and silver ions and an accompanying increasing peak yielding an isobestic point at 746 nm upon protonation. The phthalocyanine doped thin films selectively responded to silver ions in sub-nano and/or pico molar levels. Observed detection limits were 7.6 × 10⁻¹² and 2.3 × 10⁻¹¹ M for Pc-A and Pc-B, respectively. In immobilized phases the attained reversible relative signal changes of Pc-A and Pc-B were 82 and 90%, respectively.     

Quantifying spatio-temporal dynamics of solar radiation exergy over Turkey

The insensitivity to energy quality is one of the disadvantages of an energy analysis when compared to an exergy analysis. It is only the exergy analysis that clearly reveals the degradation of energy quality in the processes of absorption and emission of solar radiation. The national spatial distribution of mean monthly exergy values of solar radiation over Turkey was mapped at 500-m resolution using universal kriging based on solar radiation data from 152 geo-referenced locations. Mean exergy value of solar radiation in Turkey was estimated at 13.5 ± 1.74MJm^?2day^?1, with a mean annual exergy-to-energy ratio of 0.93.     

Selective sensing of Fe3+ at pico-molar level with ethyl cellulose based electrospun nanofibers

In this work, a new procedure for the direct determination of picomolar levels of iron in water is presented. Ethyl cellulose (EC) based nano-fibers were fabricated by electrospinning technique. The limit of detection for Fe³⁺ is 0.07 pM (based on 3 s of the blank, n = 4). A fluorescent chromoionophore and an ionic liquid were used together as sensing agent and signal stabilizer additives, respectively. The offered composite displayed a sensitive response for Fe³⁺ ions over a wide concentration range [1.0 × 10^?12–1.0 × 10^?6 M]. The sensing design exhibited a response time of less than 30 s which is one of the shortest reported responses among similar solid state sensing agents. The sensor was fully reversible and regeneration time was shorter than 60 s. Preliminary results show that sensitivity and selectivity of the nanofibrous membranes to detect Fe³⁺ ions are higher than those reported previously. Additionally, the exploited nanostructures provided faster sensor dynamics in applications.     

Feature-based rule induction in machining operation using rough set theory for quality assurance

There have been many studies, mainly by the use of statistical modeling techniques, as to predicting quality characteristics in machining operations where a large number of process variables need to be considered. In conventional metal removal processes, however, an exact prediction of surface roughness is not possible or very difficult to achieve, due to the stochastic nature of machining processes. In this paper, a novel approach is proposed to solve the quality assurance problem in predicting the acceptance of computer numerical control (CNC) machined parts, rather than focusing on the prediction of precise surface roughness values. One of the data mining techniques, called rough set theory, is applied to derive rules for the process variables that contribute to the surface roughness. The proposed rule-composing algorithm and rule-validation procedure have been tested with the historical data the company has collected over the years. The results indicate a higher accuracy over the statistical approaches in terms of predicting acceptance level of surface roughness.     

A comprehensive review of thin-layer drying models used in agricultural products

Drying is one of the widely used methods of grain, fruit, and vegetable preservation. The important aim of drying is to reduce the moisture content and thereby increase the lifetime of products by limiting enzymatic and oxidative degradation. In addition, by reducing the amount of water, drying reduces the crop losses, improves the quality of dried products, and facilitates its transportation, handling, and storage requirements. Drying is a process comprising simultaneous heat and mass transfer within the material, and between the surface of the material and the surrounding media. Many models have been used to describe the drying process for different agricultural products. These models are used to estimate drying time of several products under different drying conditions, and how to increase the drying process efficiency and also to generalize drying curves, for the design and operation of dryers. Several investigators have proposed numerous mathematical models for thin-layer drying of many agricultural products. This study gives a comprehensive review of more than 100 different semitheoretical and empirical thin-layer drying models used in agricultural products and evaluates the statistical criteria for the determination of appropriate model.     

Modified Hyaluronic Acid-Laminin-Hydrogel as Luminal Filler for Clinically Approved Hollow Nerve Guides in a Rat Critical Defect Size Model

Hollow nerve guidance conduits are approved for clinical use for defect lengths of up to 3 cm. This is because also in pre-clinical evaluation they are less effective in the support of nerve regeneration over critical defect lengths. Hydrogel luminal fillers are thought to improve the regeneration outcome by providing an optimized matrix inside bioartificial nerve grafts. We evaluated here a modified hyaluronic acid-laminin-hydrogel (M-HAL) as luminal filler for two clinically approved hollow nerve guides. Collagen-based and chitosan-based nerve guides were filled with M-HAL in two different concentrations and the regeneration outcome comprehensively studied in the acute repair rat sciatic nerve 15 mm critical defect size model. Autologous nerve graft (ANG) repair served as gold-standard control. At 120 days post-surgery, all ANG rats demonstrated electrodiagnostically detectable motor recovery. Both concentrations of the hydrogel luminal filler induced improved regeneration outcome over empty nerve guides. However, neither combination with collagen- nor chitosan-based nerve guides resulted in functional recovery comparable to the ANG repair. In contrast to our previous studies, we demonstrate here that M-HAL slightly improved the overall performance of either empty nerve guide type in the critical defect size model.     

Effect of Steam Baking on Acrylamide Formation and Browning Kinetics of Cookies

Abstract: Effects of baking method and temperature on surface browning and acrylamide concentration of cookies were investigated. Cookies were baked in natural and forced convection and steam‐assisted hybrid ovens at 165, 180, and 195 °C and at different times. For all oven types, the acrlyamide concentration and surface color of cookies increased with increasing baking temperature. Significant correlation was observed between acrylamide formation and browning index, BI, which was calculated from Hunter L, a, and b color values, and it showed that the BI may be considered as a reliable indicator of acrylamide concentration in cookies. Acrylamide formation and browning index in cookies were considered as the first‐order reaction kinetics and the reaction rate constants, k, were in the range of 0.023 to 0.077 (min^?1) and 0.019 to 0.063 (min^?1), respectively. The effect of baking temperature on surface color and acrylamide concentration followed the Arrhenius type of equation, with activation energies for acrylamide concentration as 6.87 to 27.84 kJ/mol; for BI value as 19.54 to 35.36 kJ/mol, for all oven types. Steam‐assisted baking resulted in lower acrylamide concentration at 165 °C baking temperature and lower surface color for all temperatures. Steam‐assisted baking is recommended as a healthy way of cooking providing the reduction of harmful compounds such as acrylamide for bakery goods, at a minimal level, while keeping the physical quality. Practical Application: The kinetics of acrylamide formation and browning of cookies will possibly allow definition of optimum baking temperatures and times at convectional and steam‐assisted baking ovens. The kinetic model can be used by developing baking programs that can automatically control especially a new home‐scale steam‐assisted hybrid oven producing healthy products, for the use of domestic consumers.