Identification and logging evaluation of poor reservoirs in X Oilfield

The reservoirs of X Oilfield have the characteristics of fine lithology particles, strong pore structure heterogeneity, and high argillaceous reservoirs and thin layers are generally developed. Conventional logging interpretation cannot make a fine evaluation, which results in serious discrepancies between the interpretation results of some reservoirs and actual production performance, and reserves are underestimated. Improving poor reservoir identification and logging evaluation accuracy is of great significance to oilfield development. The flow zone indicator (FZI) is used to classify the reservoirs into three types, I, II, and III, and the classification results are combined to establish a reservoir type identification chart based on logging curves; the resolution matching method and the deconvolution method are used to improve the accuracy of thin-layer recognition. Finally, the logging interpretation model is reestablished. Logging evaluations were conducted on 20 wells in X oilfield, and Y core wells were used for verification. The application results show that this method can effectively improve the identification accuracy of thin oilfields and high argillaceous reservoirs; the results of fine logging interpretation of poor reservoirs are consistent with core analysis conclusions and actual production conditions, which are typical of the successful application of poor reservoir technology.

Comprehensive identification of CO2 nonhydrocarbon gas layers by relative CO2 content and apparent porosity calculated from well logs: A case study in the Baiyun deep-water area in the eastern South China Sea

The accurate identification of [Formula: see text] nonhydrocarbon gas layers plays an important role in the exploration and development of natural gas reservoirs. The logging response characteristics of layers containing nonhydrocarbon gas and hydrocarbon gas are very similar because there are gas fluids in both kinds of layers, which leads to the uncertainty in the interpretation results of gas types. We have developed a comprehensive method for the identification of [Formula: see text] nonhydrocarbon gas layers by using [Formula: see text]-relative content and apparent porosity. First, based on the formation component bulk volume model of the coexisting reservoir of the [Formula: see text] nonhydrocarbon gas layers and the hydrocarbon gas layers, we develop an optimization algorithm to quantitatively calculate the [Formula: see text] relative content in the reservoir. Second, by analyzing and determining the logging response value of the [Formula: see text] nonhydrocarbon gas under the formation temperature and pressure conditions, we establish a qualitative identification chart using the difference between the calculated value of the apparent porosity of [Formula: see text] and the hydrocarbon gas. Finally, we were able to accurately identify the [Formula: see text] nonhydrocarbon gas layers by comprehensively analyzing the calculation results of the [Formula: see text] relative content in the reservoir and the identification results of the [Formula: see text] apparent porosity identification chart. We apply the above workflow in the Baiyun deep-water area in the eastern South China Sea, and it shows high efficiency in the identification of [Formula: see text] nonhydrocarbon gas layers.

Species of Malva L. (Malvaceae) Cultivated in the Western of Santa Catarina State and Conformity With Species Marketed as Medicinal Plants in Southern Brazil

The Malva genus presents different species with therapeutic potential and inadequate consumption can occur due to the incorrect identification of the plant in the market. The objective of this study was to identify species of the Malva genus cultivated in the Western Mesoregion of Santa Catarina State-Southern Brazil, and to verify the conformity of products’ labels marketed as dehydrated medicinal plants through the characteristics of the plant parts. The specimens were collected following an unsystematic procedure from households of ten municipalities. The material was identified with the help of specialized bibliography and a specialist. After, they were herborized and deposited in the herbaria of the State University of Santa Catarina (LUSC) and of the Botanical Garden Research Institute of Rio de Janeiro (RB). Five cultivated species of Malva were found (M. nicaeensis, M. parviflora, M. pseudolavatera, M. Sylvestris and M. verticillata). Whereas one species of the Geraniaceae family (Pelargonium graveolens) and three species of the Malvaceae family (Malva sylvestris, M. verticillata and Sida cordifolia) were found in the dehydrated packages. S. cordifolia species was present in 80% of the samples, with 65% of them marketed as M. sylvestris or under the common name of "mallow". Macroscopic images of the species and illustrations of the mericarps were made and an identification chart along with the morphological description for each species was elaborated based on the morphological results. Descriptions of labels for 60 samples were checked for the determination of the dehydrated Malva species marketed. Characteristics of parts of the plant in comparing them with the characteristics described in the specialized literature were performed. The target species of incorrect identifications in the analyzed packages was M. sylvestris.

Evaluation of Shale Around Dange, Northwestern Nigeria as Raw Material for Drilling Mud

Shale exposed around Dange northwestern, Nigeria was studied. The aim was to determine the grade, quality and usability of the shale as drilling mud using its physico-chemical and the rheological characteristics. Sixty-one (61) samples were collected from exposed sections and pits. Major elements such as SiO2, Al2O3, MgO and CaO were determined. X-ray diffraction method was employed for the qualitative identification of the minerals in the shale. Cation Exchange Capacity and Exchangeable cations were determined using the Methylene Blue Absorption and Ammonium Acetate Saturation Methods respectively. Grain-size distribution and Atterberg Limits tests were also conducted. Apparent Viscosity, Plastic Viscosity and Yield point were calculated from readings obtained on a multi-speed Fann viscometer. The dominant clay mineral observed is smectite (71%) implying good expansive potential. Dominant oxides were SiO2 (42.60-57.50%), Al2O3 (12.00-16.70%), CaO (0.43-12.50%). Exchangeable cations obtained were Na+ (0.48-6.67), Ca2+ (14.03-34.50) and K+ (0.42-1.95) meq/100g clay. Liquid and plastic limits ranged from 69.40-86.00% and 30.10-36.10% respectively giving a Plasticity Index of 39.40-52.60%. The samples plot close to Ca-montmorillonite on the clay identification chart. The viscosity increased on addition of 7-10% sodium carbonate. It has good sodium exchange potential. In the natural state, it does not meet the API/OCMA specifications. However, its rheological properties improved significantly on treatment with sodium carbonate thus confirming its suitability as raw material in drilling mud.

Based on the Sedimentary Facies of the Low Porosity and Permeability Reservoir Water Flooded Layer Qualitative Interpretation Method

Yushulin oilfield Fuyang reservoir belongs to typical low porosity and permeability reservoirs. The internal sand body has complex pore structure and serious heterogeneity. The well logging response doesn't change significantly after the reservoir is flooded. In order to improve the identification precision of the water flooded layer, this paper divides the reservoir into the thick reservoir which is mainly to fluvial facies and estuarine dam deposition and the thin and poor reservoir which is mainly to sheet sand and far sand dam two class. The qualitative identification chart of water flooded layer was established. The chart coincidence rate is more than 75 %, providing a strong basis for perforation scheme of infill wells. The research method of the peripheral oilfield flooded layer identification has wide applicability.

Fault identification in rotating machinery using artificial neural networks

In this paper, an artificial neural network system is designed and employed for fault prediction of rotating machinery systems. Multi-layer feedforward networks, constituted of non-linear neurons, have been employed. A normalization scheme is implemented on the input and output vectors. The performance of the expert structure is optimized to encounter input data with different intensities and non-regular data. More than 40 rotating machinery faults are introduced into the algorithm. To train the network, the data in the vibration identification chart consisting of vibration signals of common rotating machinery faults are used. Computer software is developed to detect machinery faults by using the above techniques and is validated for fault detection of different machinery systems. It is found that the designed network is capable of identifing unknown faults in rotating machinery. The effectiveness of the proposed neural network algorithm is displayed by several tests.

A Very Active T Tauri Star in NGC 7129

The star forming region NGC 7129 was first studied by Herbig (1960), who discovered two bright (BD+65° 1637 and LkHα234) and some weaker Hα emission stars. Subsequent investigations in this region led to the detection of five Herbig-Haro objects (Strom et al. 1974, Gyulbudaghian et al. 1978, Eiroa et al. 1992) and one T Tauri star, V350 Cep (Gyulbudaghian & Sarkissian 1977). Some non-stable stars in NGC 7129 are surrounded by reflection and cometary nebulae.Our Hα-emission survey (Semkov &: Tsvetkov 1986) revealed an interesting irregular variable star in the dark clouds near the emission nebulae. On objective prism plates the star shows very strong Hα-emission and was included in our list of such stars as No. 7. An identification chart and the coordinates can be found in Semkov (1993a). The observations reported here include long-time photographic photometry and CCD spectroscopic and photometric observations.