Petrophysical Properties and Digenetic Evolution of Shuaiba Formation in Nasiriyah Oil Field, Southern Iraq

Shuaiba Formation is a carbonate succession deposited within Aptian Sequences. This research deals with the petrophysical and reservoir characterizations characteristics of the interval of interest in five wells of the Nasiriyah oil field. The petrophysical properties were determined by using different types of well logs, such as electric logs (LLS, LLD, MFSL), porosity logs (neutron, density, sonic), as well as gamma ray log. The studied sequence was mostly affected by dolomitization, which changed the lithology of the formation to dolostone and enhanced the secondary porosity that replaced the primary porosity. Depending on gamma ray log response and the shale volume, the formation is classified into three zones. These zones are A, B, and C, each can be split into three rock intervals in respect to the bulk porosity measurements. The resulted porosity intervals are: (I) High to medium effective porosity, (II) High to medium inactive porosity, and (III) Low or non-porosity intervals. In relevance to porosity, resistivity, and water saturation points of view, there are two main reservoir horizon intervals within Shuaiba Formation. Both horizons appear in the middle part of the formation, being located within the wells Ns-1, 2, and 3. These intervals are attributed to high to medium effective porosity, low shale content, and high values of the deep resistivity logs. The second horizon appears clearly in Ns-2 well only.

THE APPLICATION OF THE SEMIPARAMETRIC GSTAR MODEL IN DETERMINING GAMMA-RAY LOG DATA ON SOIL LAYERS

This research examines the semiparametric Generalized Space-Time Autoregressive (GSTAR) spacetime modeling and determines its spatial weight. In general, the spatial weights used are uniform, binary weights, and based on the distance, the result is a fixed weight. The GSTAR model is a stochastic model that takes into account its random variables. Thus, it is necessary to study the random spatial weights. This study introduced a new method to estimate the observed value of the GSTAR model semiparametric with a uniform kernel. The data involved the Gamma Ray (GR) log data on four coal drill holes. The semiparametric GSTAR modeling aimed to predict the amount of log GR in the unobserved soil layer based on the observation data information on the layer above it and its surrounding location. The results revealed that semiparametric GSTAR modeling could predict the presence of coal seams and their thickness of drill holes. The results also highlight the validity test on the out-sample data that the error in each borehole results in a small error. In addition, the error tends to approach the actual observed value at a depth of 1 meter down.

Identification of Geo – Hazard Using Pore Pressure Analysis in ‘MAC’ Field, Niger Delta

Identification of geo-hazard zones using pore pressure analysis in ‘MAC’ field was carried out in this research. Suite of wireline logs from four wells and RFT pressure data from two wells were utilized. Lithologic identification was done using gamma ray log. Resistivity log was used to delineate hydrocarbon and non-hydrocarbon formations. Well log correlation helps to see the lateral continuity of the sands. Pore pressure prediction was done using integrated approaches. The general lithology identified is alternation of sand and shale units. The stratigraphy is typical of Agbada Formation. Three reservoirs delineated were laterally correlated. Crossplot of Vp against density (Rho) colour coded with depth revealed that disequilibrium compaction is the main overpressure generating mechanism in the field. Prediction of overpressure by normal compaction trend was generated and plot of interval transit time against depth show that there is normal compaction from 250m to about 1700 m on MAC-01, but at a depth of about 1800m, there was abnormal pressure build up that shows the onset of overpressure. A relatively normal compaction was observed on MAC-02 until a depth of about 2100m where overpressure was suspected. The prediction of formation pore pressure using Eaton’s and Bower’s method to determine the better of the two methods to adopt for pore pressure prediction shows that the pore pressure prediction using Eaton’s method gave a better result similar to the acquired pressure in the field. Hence Eaton’s method appears to be better suited for formation pore pressure estimation in ‘MAC’ field. The validation of the pore pressure analysis results with available acquired pressure data affirmed the confidence in the interpreted results for this study.

The Use of Gamma Ray Log Data to Compute and Study the Deposition of Shale Volume of Mishrif Formation in Middle and Southern Parts of Iraq

The shale volume is one of the most important properties that can be computed depending on gamma ray log. The shale volume of Mishrif Formation (carbonate formation from middle Cenomanian- early Turonian) was studied for the regional area of the middle and southern parts of Iraq. The gamma ray log data from seventeen wells ( Kf-3,Kf-4, Ad-1,Ad -2,Dh-1, Bu-47, Ns-2, Ns-4, Am-1,Am-2,Hf-2,Hf-115,Mj-3,Mj-15, Su-7,Wq-15 and Lu-7) distributed in the study area were used to compute the shale volume of Mishrif Formation. From the available data of the considered wells, a regional isopach map of Mishrif Formation was obtained. The isopach map indicates that the maximum thickness of Mishrif Formation is located at the eastern part of the study area. The results of the CPI and the shale volume map, which were computed using the Techlog and surfer software, show that the maximum value of shale volume is located at the southern part of the study area (Su-7 well), while the minimum value is at the eastern part (Hf-2well). According to the classification of Kamel and Mabrouk (2003), Mishrif Formation seems to be a Shaly Formation in the study area, except Halfaya oil field at the eastern part of the study area, which seems as a Clear Formation. The top map of the shale marker bed, which appears in most studied wells, shows a regional trend of the formation toward the northeast. According to the variation of the thickness of the shale marker bed, the study area is divided into four zones.

Lithological Study of Mishrif, Rumaila, Ahmadi and Mauddud Formations Using Vp/ Vs Ratio, Acoustic Impedance and Well Logging at Amara Oil Field, Southern Iraq

The lithology of four formations from the Cretaceous period (Mishrif, Rumaila, Ahmadi, and Mauddud) was evaluated using the Acoustic Impedance and Vp/Vs ratio cross plot from Rock Physics Templates. Dipole sonic logs in Am-6-Am-10 well log were used to calculate compression velocity then the estimated shear velocity using Greenberg Castagna equations. RHOB and VP logs were used to calculate Acoustic Impedance. The ratio of Vp/Vs was measured then used with Acoustic Impedance colored by shale volume which is measured from gamma ray log, porosity and water saturation to estimate lithology type of the considered formations using cross plots and rock physics chart in the Techlog software. The lithology of the formations found to be of high porosity limestone alternating with hard limestone layers and the shale volume increases in the Ahmadi formation. The water bearing zone was found in all Formations, this zone is indicted by high Vp/ Vs ratio and low AI. The hydrocarbon bearing zones were indicated by low amount of both Acoustic Impedance and Vp/Vs ratio and this observation was shown in Mishrif and Mauddud formations.

High-resolution late Paleozoic cyclostratigraphy and tectonic evolution of the Keeler Basin, California, southwest Laurentia

Cyclic strata exposed in the Inyo Mountains of eastern California contain a continuous 6 m.y. record of deep marine deposition that spans the Pennsylvanian−Permian boundary. To better understand the geologic evolution of southwest Laurentia and the role of glacially driven eustasy in upper Paleozoic stratigraphy, we measured two detailed ∼600 m composite stratigraphic sections of the Keeler Canyon Formation and collected a handheld spectral gamma ray log. Post-depositional deformation complicates field relationships, but 1:5000 scale mapping of faults and folds permits assembly of two continuous sections. Measured strata alternate at the 5−20 m scale between intervals of fine-grained laminated marlstone and intervals of mixed carbonate and siliciclastic turbidites and debrites. Based on facies characteristics and a prominent marker horizon, we reassign the Pennsylvanian-Permian age upper Salt Tram unit of the upper Keeler Canyon Formation to a new Estelle Member. We estimate sediment accumulation rates within the Keeler Canyon Formation using published conodont biostratigraphy and correlative U-Pb geochronology from Eastern Europe combined with spectral analysis and timescale optimization using the Astrochron R package. Evolutive harmonic analysis of gamma ray-derived element concentrations reveals prominent bundled periodicities that are consistent with both long and short eccentricity cycles. Average sediment accumulation rates calculated using the time scale optimization function of Astrochron suggest a gradual increase from 40−60 m/m.y. to ∼120 m/m.y. during the late Pennsylvanian and then a minima of ∼50 m/m.y. near the Pennsylvanian−Permian boundary, which is followed by an increase to ∼175 m/m.y. into the Early Permian. This trend in sediment accumulation rates and subsequent Permian contractile deformation are compatible with flexural subsidence in a SW-migrating foreland basin related to the southern part of the late Antler orogenic system.

A Comparison of Machine Learning Approaches for Prediction of Permeability using Well Log Data in the Hydrocarbon Reservoirs

Permeability is a vital parameter in reservoir engineering that affects production directly. Since this parameter's significance is obvious, finding a way for accurate determination of permeability is essential as well. In this paper, the permeability of two notable carbonate reservoirs (Ilam and Sarvak) in the southwest of Iran was predicted by several different methods, and the level of accuracy in all models was compared. For this purpose, Multi-Layer Perceptron Neural Network (MLP), Radial Basis Function Neural Network (RBF), Support Vector Regression (SVR), decision tree (DT), and random forest (RF) methods were chosen. The full set of real well-logging data was investigated by random forest, and five of them were selected as the potent variables. Depth, Computed gamma-ray log (CGR), Spectral gamma-ray log (SGR), Neutron porosity log (NPHI), and density log (RHOB) were considered efficacious variables and used as input data, while permeability was considered output. It should be noted that permeability values are derived from core analysis. Statistical parameters like the coefficient of determination ( ), root mean square error (RMSE) and standard deviation (SD) were determined for the train, test, and total sets. Based on statistical and graphical results, the SVM and DT models perform more accurately than others. RMSE, SD and R2values of SVM and DT models are 0.38, 1.63, 0.97 and 0.44, 2.89, and 0.96 respectively. The results of the best-proposed models of this paper were then compared with the outcome of the empirical equation for permeability prediction. The comparison indicates that artificial intelligence methods perform more accurately than traditional methods for permeability estimation, such as proposed equations. Doi: 10.28991/HEF-2021-02-02-01 Full Text: PDF