A Workflow for Static Reservoir Modeling Guided by Seismic Data in a Fluvial System

Realistic and accurate static geologic models are an essential element needed to predict the behavior of subsurface reservoirs and play an important role in petroleum engineering. Data used in the development of a static geologic model are gathered from various sources, such as seismic, log, and core data, each of them providing information on different physical properties of interest and with varying degrees of resolution. Compiling all data from various sources into a single representation of the subsurface formation of interest is a daily challenge for many petroleum geologists and engineers. This paper describes a framework to develop and select process-mimicking models that are consistent with available seismic attributes, namely impedance. Using a process-mimicking modeling package, 75 models of a fluvial meandering system are generated, one of which is chosen as the “true” model and masked thereafter. The implemented selection method relies on the degree of similarity in the histogram of representations of clusters of all possible patterns in the seismic impedance domain based on each process-mimicking model and that of the “true” model at several resolutions. The results demonstrate the effectiveness of the use of a weighted average divergence distance across multiple levels to select process-mimicking models that honor seismic data the best.     

Geochemical,isotopic and hydrological mass balance approaches to constrain the lake water–groundwater interaction in Dal Lake,Kashmir Valley

It is important to have qualitative as well as quantitative understanding of the hydraulic exchange between lake and groundwater for effective water resource management. Dal, a famous urban fresh water lake, plays a fundamental role in social, cultural and economic dynamics of the Kashmir Valley. In this paper geochemical, isotopic and hydrological mass balance approaches are used to constrain the lake water–groundwater interaction of Dal Lake and to identify the sources of lake water. Water samples of precipitation (n = 27), lake water (n = 18) and groundwater (n = 32) were collected across the lake and its catchment for the analysis of δ¹⁸O and δ²H. A total of 444 lake water samples and 440 groundwater samples (springs, tube wells and dug wells) were collected for the analysis of Ca²⁺, Mg²⁺, HCO₃ ^?, SO₄ ^2?, Cl^?, NO₃ ^?, Na⁺ and K⁺. Water table and lake water level were monitored at 40 observation locations in the catchment. Water table map including pH and EC values corroborate and verify the gaining nature of the Dal Lake. Stable isotopes of lake water in Boddal and Gagribal basins showed more deviation from the global meteoric water line than Hazratbal and Nigeen basins, indicating the evaporation of lake water. The isotopic and geochemical mass balance suggested that groundwater contributes a significant proportion (23–40%) to Dal Lake. The estimated average groundwater contribution to Dal Lake ranged from 31.2 × 10³ to 674 × 10³ m³ day^?1 with an average of 276 × 10³ m³ day^?1. The study will be useful to delineate the possible sources of nutrients and pollutants entering the lake and for the management of lake water resources for sustainable development.     

Benthic foraminiferal response to changes in mining pattern: a case study from the Zuari estuary,Goa, India

Being sensitive to environmental changes, foraminifera have been extensively used to monitor pollution level in the marine environment, including the effect of mining in coastal areas. In the Goa state of India, the rejects from opencast mining on land largely find their way to the estuaries, as washout during monsoon. Additionally, the Mormugao Port at the mouth of the Zuari estuary is the hub of activities due to the transport of ore from hinterland areas by barges and its subsequent loading for export. On the directive of the Supreme Court of India, all the mining-related activities abruptly stopped throughout India, including that in Goa in 2012, and got reinstated in 2015. Therefore, it provided a fit case to test the effectiveness of benthic foraminifera as an indicator of environmental impact due to mining activities. A total of ten surface sediment samples from five locations in Zuari estuary were collected from a depth range of 4.5–8.5 m in the years of 2013 and 2016 and were analyzed for both the living (stained) and dead benthic foraminifera. The year 2013 represents a time interval immediately after the closure of extensive mining activity, and the sampling during 2016 represents minimal mining. The living benthic foraminiferal abundance was higher (19–54/g sediment) during 2013 and decreased substantially during 2016 (3–22/g sediment), suggesting an adverse effect of activities associated with mine closure on benthic foraminifera. Additionally, the relative abundance of Ammonia was also significantly low during the year 2016. The temporal variation in dead foraminifera was, however, different than that of the living foraminifera. The differential response was attributed to the terrigenous dilution as a result of change in sedimentation rate. Therefore, we conclude that living foraminifera correctly incorporate the changes in mining pattern and may be used as an effective tool to monitor the impact of mining. We further suggest that the potential counter effect of terrigenous dilution on total and living benthic foraminiferal population should be considered while interpreting temporal variations in foraminiferal abundance in marginal marine settings.     

Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River

Accurate prediction of the chemical constituents in major river systems is a necessary task for water quality management, aquatic life well-being and the overall healthcare planning of river systems. In this study, the capability of a newly proposed hybrid forecasting model based on the firefly algorithm (FFA) as a metaheuristic optimizer, integrated with the multilayer perceptron (MLP-FFA), is investigated for the prediction of monthly water quality in Langat River basin, Malaysia. The predictive ability of the MLP-FFA model is assessed against the MLP-based model. To validate the proposed MLP-FFA model, monthly water quality data over a 10-year duration (2001–2010) for two different hydrological stations (1L04 and 1L05) provided by the Irrigation and Drainage Ministry of Malaysia are used to predict the biochemical oxygen demand (BOD) and dissolved oxygen (DO). The input variables are the chemical oxygen demand (COD), total phosphate (PO₄), total solids, potassium (K), sodium (Na), chloride (Cl), electrical conductivity (EC), pH and ammonia nitrogen (NH₄-N). The proposed hybrid model is then evaluated in accordance with statistical metrics such as the correlation coefficient (r), root-mean-square error, % root-mean-square error and Willmott’s index of agreement. Analysis of the results shows that MLP-FFA outperforms the equivalent MLP model. Also, in this research, the uncertainty of a MLP neural network model is analyzed in relation to the predictive ability of the MLP model. To assess the uncertainties within the MLP model, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPU) and the band width of 95 percent confidence intervals (d-factors) are selected. The effect of input variables on BOD and DO prediction is also investigated through sensitivity analysis. The obtained values bracketed by 95PPU show about 77.7%, 72.2% of data for BOD and 72.2%, 91.6% of data for DO related to the 1L04 and 1L05 stations, respectively. The d-factors have a value of 1.648, 2.269 for BOD and 1.892, 3.480 for DO related to the 1L04 and 1L05 stations, respectively. Based on the values in both stations for the 95PPU and d-factor, it is concluded that the neural network model has an acceptably low degree of uncertainty applied for BOD and DO simulations. The findings of this study can have important implications for error assessment in artificial intelligence-based predictive models applied for water resources management and the assessment of the overall health in major river systems.     

Support vector machines and feed-forward neural networks for spatial modeling of groundwater qualitative parameters

The present study attempts to model the spatial variability of three groundwater qualitative parameters in Guilan Province, northern Iran, using artificial neural networks (ANNs) and support vector machines (SVMs). Data collected from 140 observation wells for the years 2002–2014 were used. Five variables, X and Y coordinates of the observation well, distance of the observation well from the shoreline, areal average 6-month rainfall depth, and groundwater level at the day of water quality sampling, were considered as primary input variables. In addition, nine qualitative variables were also considered as auxiliary input variables. Electrical conductivity (EC), sodium concentration (Na⁺), and sulfate concentration (SO₄ ^2?) of the groundwater in the region were estimated using ANNs and SVMs with different input combinations. The results showed that both ANNs and SVMs work well when the only primary input variable is the well location. The ANN yielded an RMSE of 1.03 mEq/l for SO₄ ^2?, 1.05 mEq/l for Na⁺, and 203.17 μS/cm for EC, using the X and Y coordinates of the observation wells in the study area. In the case of SVM, these values were, respectively, 0.87, 0.87, and 176.68. Considering the auxiliary input variables (pH, EC, and the concentrations of Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl^?, SO₄ ^2?, and HCO₃ ^?) resulted in a significant decrease in the RMSE of both ANNs (0.22, 0.30, and 33.04) and SVMs (0.26, 0.34, and 36.23). Comparing these RMSE values with those of cokriging interpolation technique (0.59, 0.98, and 177.59) indicated that ANNs and SVMs produced more accurate estimates of the three qualitative parameters. The relative importance of auxiliary input variables was also determined using Gamma test. The output uncertainty of ANNs and SVMs were determined using p-factor and d-factor. The results showed that SVMs have less uncertainty than ANNs.     

Wrench tectonics of Abu Gharadig Basin,Western Desert,Egypt: a structural analysis for hydrocarbon prospects

Mapping based on the interpreted seismic data covering the Abu Gharadig Basin in the northern Western Desert has revealed that the deposition of the Upper Cretaceous succession was controlled by dextral wrench tectonics. This dextral shear accompanied NW movement of the African Plate relative to Laurasian Plate. Structural depth maps of the Cenomanian Bahariya Formation and the Turonian-Coniacian D and A members of Abu Roash Formation display a clear NE-SW anticline dissected by NW-SE normal faults. This anticline represents one of the en echelon folds characterizing the wrench compressional component. The interpreted normal faults reflect the extensional T-fractures associated with the wrenching tectonics. The interaction between the aforementioned NE-SW anticline with the NW-SE extensional faults further confirms the effect of the Upper Cretaceous dextral wrench tectonic. However, the influence of this wrench tectonics was gradually diminishing from the Cenomanian up to the Coniacian times. The NW-SE compressional stress of the dextral wrench compressional component during the Cenomanian up to Coniacian age was greater in NW direction than the SE direction. Three mapped structural closures which are predicted to be potential hydrocarbon traps belonging to the Bahariya Formation and Abu Roash D Member, and are recommended to be drilled in the study area, with potential reservoirs. The regularity of the en echelon array of both anticlines and normal faults within the wrench zones suggests additional closures may be located elsewhere beside the study area.     

Applying an optimized proxy-based workflow for fast history matching

History matching is still one of the main challenging parts of reservoir study especially in giant brown oil fields with lots of wells. In these cases, history matching with conventional manual technique needs many runs and takes months to get a match. In this work, an innovative approach was suggested for fast history matching in a real brown field. The workflow was employed based on an optimized proxy model for history matching of a field consisting of 14 active wells with multiple responses (which are production rate and pressure data) in the south part of Iran. The main important features of the proposed algorithm were defining a proxy model which is response surface method in which 21 model parameters were incorporated based on cubic centered face method. The proxy model was then optimized by one of the most famous algorithms which is genetic algorithm. Proxy model was successfully performed using 256 samples leading into p- value of 0.531 and R ² of 0.91 dataset. As a result, the proposed workflow and algorithm showed good and acceptable results for history matching of studied real model.     

Seismic vulnerability assessment in the new urban area of Diriyah Governorate,Riyadh, Saudi Arabia

Nine seismic refraction profiles were conducted and processed to study the near-surface sediments in the new urban area of Diriyah. The 2D geoseismic models illustrate two layers: a surface layer of soft sediments and weathered to hard limestone bedrock. Moreover, microtremor measurements were performed at 38 sites for 40 min using three-component seismographs and processed to assess the peak spectral amplitude and the corresponding fundamental resonance frequency. The seismic vulnerability index at each measurement site was estimated. These results correlate well with the geotechnical borehole data. The north-western zone is highly vulnerable due to the great thickness of the soft sediments.     

Denoising and improving the quality of seismic data using combination of DBM filter and FX deconvolution

Seismic data denoising, random noise attenuation (RNA) and spike-like noise suppression, is a main consideration for improving the quality of records. RNA could increase signal to noise ratio (S/N) to avoid misinterpretation of seismic data. In this research, a novel method is created by using the combination of frequency-offset deconvolution (FXD) and decision-based median (DBM) filter for RNA from seismic data. The method is applied in two main phases; FXD is focused to remove the Gaussian noise and DBM filter is focused to attenuate the impulsive noise and spikes. To implement and verify the method, three types of data are used: two synthetic models (a model with linear events and a model with hyperbolic events) and an observed seismic section. The ability of the proposed method (FXD-DBM) in comparison of applying each in seismic RNA application is proven. The noise level is reduced obviously, and hence, the S/N of all examined seismic records is increased considerably after denoising by the combination of FX deconvolution and DBM filter. About the real seismic section, suppressing random noise and spikes show up improving the seismic reflector continuity and hence enhancing the interpretability of data. Moreover, some masked events by random noise are clarified in different parts of data after denoising using the planned method.