Petrophysical and rock physics analyses for characterization of complex sands in deepwater Niger delta, Nigeria

Characterization of complex sand reservoirs in deepwater of Niger Delta was carried out through petrophysical and rock physics evaluation of well log data from three wells. Petrophysical analysis to determine clay volume, porosity, lithologies and hydrocarbon saturation were made. Rock physics was studied in velocity-porosity plane to analyze the influence of depositional and diagenetic features on the reservoirs. Cross-plots of different elastic parameters, using linear regression and cluster analysis, were generated for lithologic and fluid fill identification and to differentiate between the hydrocarbon bearing sands, brine sands and shale. Variance attribute was extracted on seismic time slice in order to image the complex sand distribution in the area. Three reservoirs of turbidite origin were identified within the upper fan to lower fan area. Petrophysical results revealed gas bearing reservoir units with less than 20% shale volume and porosity of 25-31%. Lambda-Mu-Rho (LMR) cross-plots for the reservoirs show gas saturated data cloud and trend. Ratio-Difference (R-D) cluster analysis of elastic rock properties shows a distinct trend and data cloud that represents lithofacies units and fluid fills. The study concludes that the reservoirs simulated contact cement and friable models with properties that ranged from highly porous, well sorted and poorly consolidated sand to fairly sorted and highly cemented sands. The results provide a model that increases the possibility of finding reservoir sand, while mitigating the risk involved in finding hydrocarbons

Petrophysical and rock physics analyses for characterization of complex sands in deepwater Niger delta, Nigeria

Characterization of complex sand reservoirs in deepwater of Niger Delta was carried out through petrophysical and rock physics evaluation of well log data from three wells. Petrophysical analysis to determine clay volume, porosity, lithologies and hydrocarbon saturation were made. Rock physics was studied in velocity-porosity plane to analyze the influence of depositional and diagenetic features on the reservoirs. Cross-plots of different elastic parameters, using linear regression and cluster analysis, were generated for lithologic and fluid fill identification and to differentiate between the hydrocarbon bearing sands, brine sands and shale. Variance attribute was extracted on seismic time slice in order to image the complex sand distribution in the area. Three reservoirs of turbidite origin were identified within the upper fan to lower fan area. Petrophysical results revealed gas bearing reservoir units with less than 20% shale volume and porosity of 25-31%. Lambda-Mu-Rho (LMR) cross-plots for the reservoirs show gas saturated data cloud and trend. Ratio-Difference (R-D) cluster analysis of elastic rock properties shows a distinct trend and data cloud that represents lithofacies units and fluid fills. The study concludes that the reservoirs simulated contact cement and friable models with properties that ranged from highly porous, well sorted and poorly consolidated sand to fairly sorted and highly cemented sands. The results provide a model that increases the possibility of finding reservoir sand, while mitigating the risk involved in finding hydrocarbons

Uncertainty reduction in reservoir parameters prediction from multiscale data using machine learning in deep offshore reservoirs.

Developing a complete characterization of reservoir properties involved in subsurface multiphase flow is a very challenging task. In most cases, these properties - such as porosity, water saturation, permeability (and their variants), pressure, wettability, bulk modulus, Young modulus, shear modulus, fracture gradient - cannot be directly measured and, if measured, are available only at small number of well locations. The limited data are then combined with geological interpretation to generate a model. Also increasing the degree of this uncertainty is the fact that the reservoir properties from different data sources - like well logs, cores and well test - often produce different results, thus making predictions less accurate. The present study focussed on three reservoir parameters: porosity, fluid saturation and permeability. These were selected based on literature and sensitivity analysis, using Monte Carlo simulations on net present value, reserve estimates and pressure transients. Sandstone assets from the North Sea were used to establish the technique for uncertainty reduction, using machine learning as well as empirical models after data digitization and cleaning. These models were built (trained) with observed data using other variables as inputs, after which they were tested by then using the input variables (not used for the training) to predict their corresponding observed data. Root Mean Squared Error (RMSE) of the predicted and the actual observed data was calculated. Model tuning was done in order to optimize its key parameters to reduce RMSE. Appropriate log, core and test depth matching was also ensured including upscaling combined with Lorenz plot to identify the dominant flow interval. Nomographic approach involving a numerial simulation run iteratively on multiple non-linear regression model obtained from the dataset was also run. Sandstone reservoirs from the North Sea not used for developing the models were then used to validate the different techniques developed earlier. Based on the above, the degree of uncertainty associated with porosity, permeability and fluid saturation usage was demonstrated and reduced. For example, improved accuracies of 1-74%, 4-77% and 40% were achieved for Raymer, Wyllie and Modified Schlumberger, respectively. Raymer and Wyllie were also not suitable for unconsolidated sandstones while machine learning models were the most accurate. Evaluation of logs, core and test from several wells showed permeability to be different across the board, which also highlights the uncertainty in their interpretation. The gap between log, core and test was also closed using machine learning and nomographic methods. The machine learning model was then coded into a dashboard containing the inputs for its training. Their relationship provides the benchmark to calibrate one against the other, and also to create the platform for real-time reservoir properties prediction. The technology was applied to an independent dataset from the Central North Sea deep offshore sandstone reservoir for the validation of these models, with minimum tuning and thus effective for real-time reservoir and production management. While uncertainties in measurements are crucial, the focus of this work was on the intermediate models to get better final geological models, since the measured data were from the industry

Characterization of the Germania Spraberry unit from analog studies and cased-hole neutron log data

The need for characterization of the Germania unit has emerged as a first step in the review, understanding and enhancement of the production practices applicable within the unit and the trend area in general. Petrophysical characterization of the Germania Spraberry units requires a unique approach for a number of reasons ?? limited core data, lack of modern log data and absence of directed studies within the unit. In the absence of the afore mentioned resources, an approach that will rely heavily on previous petrophysical work carried out in the neighboring ET O??Daniel unit (6.2 miles away), and normalization of the old log data prior to conventional interpretation techniques will be used. A log-based rock model has been able to guide successfully the prediction of pay and non-pay intervals within the ET O??Daniel unit, and will be useful if found applicable within the Germania unit. A novel multiple regression technique utilizing non-parametric transformations to achieve better correlations in predicting a dependent variable (permeability) from multiple independent variables (rock type, shale volume and porosity) will also be investigated in this study. A log data base includes digitized formats of gamma ray, cased hole neutron, limited resistivity and neutron/density/sonic porosity logs over a considerable wide area

A PETROPHYSICAL EVALUATION FOR PERMEABILITY OF A GAS RESERVOIR IN THE TARANAKI BASIN, NEW ZEALAND

The goal of this study was to evaluate permeability and study the controls on permeability in a gas saturated formation. Conventional well logs, mineral identification crossplots and empirical models were applied to analyze different lithologic and diagenetic features and to examine the effect that these features may have on the reservoir. An unusual feature was observed, and required detailed examination: there existed (in two wells) five zones of lower resistivity (higher water saturation) above the gas-water contact. This is unexpected, as above that contact, the water is usually at irreducible water saturation. I conclude that the lower resistivity (the higher water saturation) is due to unusual mineralogy containing small grain size, ineffective microporosity and secondary porosity within specific grains, and support this conclusion with a variety of indicators. Two wells in New Zealand’s Taranaki Basin were used for this study. First, various routinely applied methods were used to assign the boundaries of the gas-saturated zones of the Mangaa C-1 sandstone and to identify the mineralogy. From Archie and non-Archie (Simandoux, Schlumberger, Indonesia, and Dual-Water) models, four subzones in Karewa-1 well and two subzones in Kahawai-1 well were recognized as high water saturation intervals within the Mangaa C-1 gas saturated formation. The analysis of saturation can be used to identify grain size (and pore size) distribution, which turned out to be critical in understanding the high water saturation zones. Bulk volume water analysis was used together with created lithology logs and with core descriptions that had been made available to recognize the detrimental diagenetic zones in the gas formation. Dissolution of minerals, grain size distributions, and different pore type characteristics increase bulk volume water in high water saturation zones while keeping the formation at irreducible water saturation, but at levels that are elevated in comparison with higher-quality (and lower irreducible water saturation) zones both above and below. The various irreducible water saturation zones were then used to predict the absolute permeability of those zones using several empirical models. Then, different flow unit characterization methods were applied to better understand the different quality rocks within the formation. One approach represents a new attempt to compare results for pore size classifications. My results showed that diagenesis is more detrimental to reservoir quality than grain size within the Mangaa C-1 gas sandstone, and that there is no transition or fully-water saturated zone under the gas reservoir at Karewa-1 well, while the transition zone exists for the same formation at Kahawai-1 well

A prediction model of specific productivity index using least square support vector machine method

In the design of oilfield development plans, specific productivity index plays a vital role. Especially for offshore oilfields, affected by development costs and time limits, there are shortcomings of shorter test time and fewer test sampling points. Therefore, it is very necessary to predict specific productivity index. In this study, a prediction model of the specific productivity index is established by combining the principle of least squares support vector machine (LS-SVM) with the calculation method of the specific productivity index. The model uses logging parameters, crude oil experimental parameters and the specific productivity index of a large number of test well samples as input and output items respectively, and finally predicts the specific productivity index of non-test wells. It reduces the errors caused by short training time, randomness of training results and insufficient learning. A large number of sample data from the Huanghekou Sag in Bohai Oilfield were used to verify the prediction model. Comparing the specific productivity index prediction results of LS-SVM and artificial neural networks (ANNs) with actual well data respectively, the LS-SVM model has a better fitting effect, with an error of only 3.2%, which is 12.1% lower than ANNs. This study can better reflect the impact of different factors on specific productivity index, and it has important guiding significance for the evaluation of offshore oilfield productivity.Cited as: Wu, C., Wang, S., Yuan, J., Li, C., Zhang, Q. A prediction model of specific productivity index using least square support vector machine method. Advances in Geo-Energy Research, 2020, 4(4): 460-467, doi: 10.46690/ager.2020.04.1

Joint interpretation of magnetotelluric, seismic, and well-log data in Hontomín (Spain)

Acknowledgements. This work is dedicated to the memory of Andrés Pérez-Estaún, brilliant scientist, colleague, and friend. The authors sincerely thank Ian Ferguson and an anonymous reviewer for their useful comments on the manuscript. Xènia Ogaya is currently supported in the Dublin Institute for Advanced Studies by a Science Foundation Ireland grant IRECCSEM (SFI grant 12/IP/1313). Juan Alcalde is funded by NERC grant NE/M007251/1, on interpretational uncertainty. Juanjo Ledo, Pilar Queralt and Alex Marcuello thank Ministerio de Economía y Competitividad and EU Feder Funds through grant CGL2014- 54118-C2-1-R. Funding for this Project has been partially provided by the Spanish Ministry of Industry, Tourism and Trade, through the CIUDEN-CSIC-Inst. Jaume Almera agreement (ALM-09-027: Characterization, Development and Validation of Seismic Techniques applied to CO2 Geological Storage Sites), the CIUDEN-Fundació Bosch i Gimpera agreement (ALM-09-009 Development and Adaptation of Electromagnetic techniques: Characterisation of Storage Sites) and the project PIERCO2 (Progress In Electromagnetic Research for CO2 geological reservoirs CGL2009-07604). The CIUDEN project is co-financed by the European Union through the Technological Development Plant of Compostilla OXYCFB300 Project (European Energy Programme for Recovery).Peer reviewedPublisher PD

Rock Classification in Organic Shale Based on Petrophysical and Elastic Rock Properties Calculated from Well Logs

This thesis introduces a rock classification technique for organic-rich shale that takes into account well-log-based estimates of compositional, petrophysical, and elastic properties. Well logs and laboratory core measurements were used to calculate depth-by-depth petrophysical and compositional properties of three wells in two organic-rich formations. Then, either acoustic well logs or effective medium theories helped estimate formation elastic properties. Estimates of total porosity, Total Organic Content (TOC), fluid saturation, volumetric concentrations of mineral constituents, and elastic properties facilitated identification of different rock classes, using an unsupervised artificial neural network. A good rock classification technique improves (a) petrophysical evaluation of organic-rich shale reservoirs, (b) fluid flow characterization, (c) detection of productive zones for fracturing jobs, and (d) prediction of hydraulic fracturing and stimulation effectiveness. Then, a rock classification method was then applied to the field examples from the Haynesville shale and Woodford shales for rock classification. The estimates of porosity, TOC, bulk modulus, shear modulus, and volumetric concentrations of minerals were obtained and then validated by comparing them to laboratory measurements. These calculated properties and well logs served as inputs to an artificial neural network to identify the different rock classes in both formations. Finally, the rock classes enabled identification of good candidate zones for fracture stimulation

Shale Gas Potential Evaluation of Goldwyer and Laurel Formations, Canning Basin, WA.

The Middle Ordovician Goldwyer Formation is known to be the richest organic shale in the Canning Basin. This dissertation identifies the favourable stratigraphic intervals of the Goldwyer shales for the purpose of future petroleum exploration and shale gas development. An integrated study of the key geological, geochemical and petrophysical characteristics is comprehensively studied and the results were used for map generation and 3D modelling of the most potential sweet spots in the Canning Basin