Naturally Fractured Basement Reservoir Characterization in a Mature Field

Characterizing the naturally fractured reservoir in a mature field is always a challenging task due to minimal subsurface data availability and the technology was not as advanced as nowadays. Therefore, this paper is proposed to provide an alternative solution to identify the presence of the fractures, classify them into the fractured quality related flowability, and distribute them vertically within the well interval and propose a lateral distribution method for reservoir modeling. This research was conducted based on a case study of basement fractured carbonate reservoir in Hungary. I used more than twenty development wells which mainly drilled during 1980-2000's. The fractures presence is simply identified by using gamma-ray and density logs. The relative movement of density log to the defined fractured baselines was directed to classify the fracture quality within three groups of macro-fracture, micro-fracture, and host-rock. These groups were validated by core data and the acoustic image log from the newest drilled wells. Furthermore, I implemented the self-organizing map (SOM) for distributing the fracture group to other wells which having limited subsurface data. Since the fracture classes were distributed along the well depth interval, then the well test (DST) results and production flow test data validated the flowability of them. As a result, the main flow contribution intervals of the fracture can be well-recognized. The macro-fracture consistently indicates the fracture class showing the main contribution of the liquid flowrate more than 10 m3/d along the perforated intervals. The rock properties of this class have porosity range around 1-2% with permeability dominantly more than 100 mD. In contrast, the host-rock class is defined as a protolith/non-fractured rock. The porosity and permeability are extremely low (tight rock). This class does not give any flow contribution due to the high content of the marl or clay, the absence of the fracture, or the fractures had been re-cemented by calcite or quartz minerals. Meanwhile, the micro-fracture denotes the group of rock with porosity range around 2-10% and permeability average between 1-10 mD. In general, the flowrate coming from this fracture class was lower than 10 m3/d of liquid during the flow-test. As a novelty, this proposed approach with the machine learning of SOM-clustering effectively assists us to recognize the fracture presence and its quality along the well-depth interval from the absence of the advanced technologies of image logs and production logging (PLT) measurement. Also, the defined fracture class here can take a role as a fracture facies or rock typing in terms of 3D reservoir modeling and distributed laterally based on fault-likelihood attribute and fault zone defined by distance-to-fault.

Microstructure characterization and properties of porous piezoceramics

In this paper, a comprehensive study of microstructure/properties interrelations for porous piezoceramics based on PZT composition was performed. Experimental samples of porous piezoceramics were fabricated using a modified method of burning-out a pore former. Porosity dependencies of elastic, dielectric, piezoelectric and electromechanical coefficients of the porous ceramics in the relative porosity range 0–50% were obtained and analyzed. As a result of microstructure analysis, it was found that at any connectivity type (3–0, 3–3) and porosity up to 50% the real structures of porous piezoceramics were close to the matrix medium structure with continuous piezoceramic skeleton. It was also revealed that the microstructural features of porous piezoceramics define the character of the dependences of the dielectric, piezoelectric and electromechanical properties of porous piezoelectric ceramics on porosity. In conclusion, microstructure/properties interrelations, as well as new applications of porous piezoceramics were discussed.

ACOUSTIC PERFORMANCES OF METAL SAMPLES MANUFACTURED BY ADDITIVE TECHNOLOGIES

Porous materials (PMs) have been widely used since the development of powder metallurgy. PM samples are obtained by various methods, while the structure varies from sample to sample. Deviation from a given structure leads to a deviation of the specified properties up to 30%, including acoustic. Selective laser melting (SLM), allows to obtain samples with a low structural deviation (up to 13%) in a wide porosity range P=0.3-0.7. An increase in the sound absorption coefficient and the expansion of the frequency range allows to use such samples in noise reduction units’ designs. Nine samples with different porosity and cell shape were obtained by the SLM method with the use of AlSi10Mg aluminum powder obtained by gas atomization and BB751P nickel powder obtained by plasma centrifugal spraying. The porosity of the samples (P) varied in the range (0.3–0.7), the diameter was D = 34.5 mm, and the height varied in the range of 15–45 mm. The acoustic characteristics comparison of traditional PMs with porous fused material (PFM) by the sound absorption index shows that PM, as a rule, are superior to PFM, while it is approximately equal to porous cast

Porosity and Permeability Measurements Integration of The Upper Cretaceous in Balad Field, Central Iraq

The corrected porosity image analysis and log data can be used to build 3D models for porosity and permeability. This can be much realistic porosity obtainable because the core test data is not always available due to high cost which is a challenge for petroleum companies and petrophysists. Thus, this method can be used as an advantage of thin section studies and for opening horizon for more studies in the future to obtain reservoir properties. Seventy-two core samples were selected and the same numbers of thin sections were made from Khasib, Sa`di, and Hartha, formations from Ba-1, Ba-4, and Ba-8 wells, Balad Oilfield in Central Iraq to make a comprehensive view of using porosity image analysis software to determine the porosity. The petrophysical description including porosity image analysis was utilized and both laboratory core test analysis and well log analysis were used to correct and calibrate the results. The main reservoir properties including porosity and permeability were measured based on core samples laboratory analysis. The results of porosity obtained from well log analysis and porosity image analysis method are corrected by using SPSS software; the results revealed good correlation coefficients between 0.684 and 0.872. The porosity range values are 9-16% and 9-27% for Khasib and Sa’di in Ba-1 Well, respectively; 10-21%, 9-25%, and 16-27% for Khasib, Sa’di and Hartha in Ba-4 Well, respectively; and 11-24% and 15-24% for Khasib and Hartha in Ba-8 Well, respectively according to petrographic image analysis. By using the laboratory core analysis, the porosity range values are 12-26% and 17-24% for Khasib and Sa’di in Ba-1 Well, respectively; 6-28% and 14-27% for Sa’di and Hartha in Ba-4 Well, respectively; and 17-19% and 15-24% for Sa’di and Hartha in Ba-8 Well, respectively. Finally, the well log analysis showed that the porosity range values are 11-16% and 7-27% for Khasib and Sa’di in Ba-1 Well, respectively; 4-18%, 21-26%, and 16-19% for Khasib, Sa’di and Hartha in Ba-4 Well, respectively; and 9-24% and 15-23% for Khasib and Hartha in Ba-8 Well, respectively. The permeability range values based on laboratory core analysis are 1.51-8.97 md and 0.29-2.77 md for Khasib and Sa’di in Ba-1 Well, respectively; 0.01-24.5 md and 0.28-6.47 md for Sa’di and Hartha in Ba-4 Well, respectively; and 0.86-2.25 md and 0.23-3.66 for Sa’di and Hartha in Ba-8 Well, respectively.

The Viking Fields, Blocks 49/11d, 49/12a, 49/16a, 49/16c, 49/17a, UK North Sea

The Viking Fields were a gas development in the UK Southern North Sea, c. 130 km east of the Lincolnshire coast in 30 m water depth and covering Blocks 49/11d, 49/12a, 49/16a, 49/16c, 49/17a. The area comprised the Viking A, B, C, D and E Fields.The Viking Fields were discovered in 1965 and started producing in 1972. The development was in two phases from 1971 to 1994 and from 1995 to 2000; the latter phase included the ‘Phoenix development’. The fields continued to produce until September 2015. Plugging and abandonment of the Viking Field wells was complete in 2017, with final decommissioning planned for 2021.The Viking Fields have produced 3.3 tcf of gas from the Rotliegend Group, Leman Sandstone Formation, aeolian-dominated reservoir rocks with a porosity range of 7–25% and average permeability of >100 mD. The Viking reservoirs are impacted by NE--SW De Keyser faults which often delineate and compartmentalize the reservoirs. The final recovery factor for the Viking Fields was 90%. This paper summarizes the geology, development history and performance of these legacy assets.

Investigating permeability of metal felt capillary structures of heat pipes for cooling electronics

The paper presents the experimental results on the permeability of metal felt capillary-porous structures with a fiber diameter of 10—50 μm at porosity values from 57% to 90% when the fluid filtration occurs along the felt plane. It is determined that the permeability depends on the geometric parameters of the capillary structure (fiber diameter), porosity and direction of fluid filtration. In previous permeability studies, no attention was paid to the direction of fluid movement in the capillary structure. It was believed that the metal felt structure is isotropic and the permeability was studied for cross-fiber filtration. In reality, unlike regular capillary structures (powder), metal felt structures are anisotropic and their characteristics depend on the direction of fluid filtration. In heat pipes, the capillary structure fibers are mostly positioned parallel to the axis of the pipe, and thus the fluid moves from the condensation zone to the evaporation zone along the fibers. It was shown that at a porosity of 55—70%, the value of permeability does not depend on the direction of filtration. In the porosity range from 70% to 90%, error can exceed 50%. In this porosity range, the permeability value at cross-fiber filtration significantly exceeds the permeability value at longitudinal filtration. This proves that the calculation relations for determining the permeability coefficients of metal felt capillary-porous structures obtained for cross-fiber filtration cannot be used to calculate heat pipes. Analyzing the results and processing the obtained experimental data allowed proposing an empirical dependence that generalizes the data with an error of up to 20% in the whole range of the studied porosity values. The research results can be used to design heat pipes with maximum heat transfer characteristics for cooling electronics.

Evaluating porosity estimates for sandstones based on X-ray micro-tomographic images

We compare experimentally determined porosity with values derived from X-ray tomography for a suite of eight sandstone varieties covering a porosity range from about 3 to 25 %. In addition, we performed conventional stereological analysis of SEM images and examined thin sections. We investigated the sensitivity of segmentation, the conversion of the tomographic gray-value images representing the obtained X-ray attenuation coefficients into binary images, to (a) resolution of the digital images, (b) denoising filters, and (c) seven thresholding methods. Images of sandstones with porosities of 15 to 25 % exhibit a bimodal intensity distribution of the attenuation coefficients, enabling unambiguous segmentation that gives porosity values closely matching the laboratory values. For samples with lower porosities, pores and grains do not separate well in the skewed unimodal intensity histograms. For these samples, all tested thresholding methods tend to miscalculate porosity significantly. In addition to absolute porosity, the ratio between pore size and resolution, and mineralogical composition of the rocks affect the biases of the global segmentation methods.

Groundwater storage in Africa

This 5 km resolution grid presents groundwater storage in Africa (in mm). This parameter was estimated by combining the saturated aquifer thickness and effective porosity of aquifers across Africa. For each aquifer flow/storage type an effective porosity range was assigned based on a series of studies across Africa and surrogates in other parts of the world. Groundwater storage is given in millimeters. Detailed description of the methodology, and a full list of data sources used to develop the layer can be found in the peer-reviewed paper available here: http://iopscience.iop.org/article/10.1088/1748-9326/7/2/024009/pdf The raster and a high resolution PDF file are available for download on the website of British Geological Survey (BGS): http://www.bgs.ac.uk/research/groundwater/international/africanGroundwater/mapsDownload.html Groundwater Storage

Permeability anisotropy and resistivity anisotropy of mechanically compressed mudrocks

Permeability anisotropy (the ratio of the horizontal to vertical permeability) develops in mudrocks at the macroscale due to heterogeneities such as layering and at the element scale (i.e., within a single homogeneous layer or unit) due to decreasing porosity and increasing platy particle alignment. This work describes new experimental methods and results from a laboratory program using cubic specimens to investigate the evolution of mudrock permeability and resistivity at the element scale. A systematic study analyzes the evolution of the permeability anisotropy and electrical resistivity anisotropy using resedimented Boston blue clay (RBBC) over a stress range of 0.4–40 MPa; additional measurements are presented for three other mudrocks within varying clay fraction and clay mineralogy within the stress range 1.2–10 MPa. The permeability anisotropy and the conductivity anisotropy (inverse of the resistivity anisotropy) of all four mudrocks studied ranges from 1 to 2.2 over the porosity range 0.55–0.30. A nearly 1:1 correlation between the electrical conductivity anisotropy and the permeability anisotropy is observed to be independent of clay fraction or clay mineralogy.