Integrated workflow approach to static modeling of Igloo R3 reservoir, onshore Niger Delta, Nigeria

2015 ◽  
Vol 3 (3) ◽  
pp. SZ1-SZ14 ◽  
Author(s):  
Emmanuel Kenechukwu Anakwuba ◽  
Clement Udenna Onyekwelu ◽  
Augustine Ifeanyi Chinwuko
Keyword(s):  
Correlation Coefficient ◽  
Acoustic Impedance ◽  
Niger Delta ◽  
Seismic Inversion ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Sequential Gaussian Simulation ◽  
Reservoir Properties ◽  
Static Modeling ◽  
Geophysical Well Logs

We constructed a 3D static model of the R3 reservoir at the Igloo Field, Onshore Niger Delta, by integrating the 3D seismic volume, geophysical well logs, and core petrophysical data. In this model, we used a combined petrophysical-based reservoir zonation and geostatistical inversion of seismic attributes to reduce vertical upscaling problems and improve the estimation of reservoir properties between wells. The reservoir structural framework was interpreted to consist of three major synthetic faults; two of them formed northern and southern boundaries of the field, whereas the other one separated the field into two hydrocarbon compartments. These compartments were pillar gridded into 39,396 cells using a [Formula: see text] dimension over an area of [Formula: see text]. Analysis of the field petrophysical distribution showed an average of 21% porosity, 34% volume of shale, and 680-mD permeability. Eleven flow units delineated from a stratigraphic modified Lorenz plot were used to define the reservoir’s stratigraphic framework. The calibration of acoustic impedance using sonic- and density-log porosity showed a 0.88 correlation coefficient; this formed the basis for the geostatistic seismic inversion process. The acoustic impedance was transformed into reservoir parameters using a sequential Gaussian simulation algorithm with collocated cokriging and variogram models. Ten equiprobable acoustic impedance models were generated and further converted into porosity models by using their bivariate relationship. We modeled the permeability with a single transform of core porosity with a correlation coefficient of 0.86. We compared an alternative model of porosity without seismic as a secondary control, and the result showed differences in their spatial distributions, which was a major control to fluid flow. However, there were similarities in their probability distribution functions and cumulative distribution functions.

scholarly journals Reservoir properties estimation from 3D seismic data in the Alose field using artificial intelligence

2021 ◽  
Vol 11 (3) ◽  
pp. 1275-1287
Author(s):  
A. Ogbamikhumi ◽  
J. O. Ebeniro
Keyword(s):  
Neural Network ◽  
Correlation Coefficient ◽  
Feasibility Study ◽  
Cross Validation ◽  
Water Saturation ◽  
Seismic Inversion ◽  
Reservoir Properties ◽  
Before And After ◽  
Secondary Constraint ◽  
Static Modeling

AbstractIn an attempt to reduce the errors and uncertainties associated with predicting reservoir properties for static modeling, seismic inversion was integrated with artificial neural network for improved porosity and water saturation prediction in the undrilled prospective area of the study field, where hydrocarbon presence had been confirmed. Two supervised neural network techniques (MLFN and PNN) were adopted in the feasibility study performed to predict reservoir properties, using P-impedance volumes generated from model-based inversion process as the major secondary constraint parameter. Results of the feasibility study for predicted porosity with PNN gave a better result than MLFN, when correlated with well porosity, with a correlation coefficient of 0.96 and 0.69, respectively. Validation of the prediction revealed a cross-validation correlation of 0.88 and 0.26, respectively, for both techniques, when a random transfer function derived from a given well is applied on other well locations. Prediction of water saturation using PNN also gave a better result than MLFN with correlation coefficient of 0.97 and 0.57 and cross-validation correlation coefficient of 0.89 and 0.3, respectively. Hence, PNN technique was adopted to predict both reservoir properties in the field. The porosity and water saturation predicted from seismic in the prospective area were 24–30% and 20–30%, respectively. This indicates the presence of good quality hydrocarbon bearing sand within the prospective region of the studied reservoir. As such, the results from the integrated techniques can be relied upon to predict and populate static models with very good representative subsurface reservoir properties for reserves estimation before and after drilling wells in the prospective zone of reservoirs.

scholarly journals Petrophysical seismic inversion based on lithofacies classification to enhance reservoir properties estimation: a machine learning approach

2020 ◽  
Author(s):  
Amir Abbas Babasafari ◽  
Shiba Rezaei ◽  
Ahmed Mohamed Ahmed Salim ◽  
Sayed Hesammoddin Kazemeini ◽  
Deva Prasad Ghosh
Keyword(s):  
Acoustic Impedance ◽  
Reservoir Characterization ◽  
Seismic Inversion ◽  
Reservoir Properties ◽  
Well Location ◽  
The Neural Network ◽  
Machine Learning Approach ◽  
Lithofacies Classification ◽  
Elastic Inversion ◽  
Significant Match

Abstract For estimation of petrophysical properties in industry, we are looking for a methodology which results in more accurate outcome and also can be validated by means of some quality control steps. To achieve that, an application of petrophysical seismic inversion for reservoir properties estimation is proposed. The main objective of this approach is to reduce uncertainty in reservoir characterization by incorporating well log and seismic data in an optimal manner. We use nonlinear optimization algorithms in the inversion workflow to estimate reservoir properties away from the wells. The method is applied at well location by fitting nonlinear experimental relations on the petroelastic cross-plot, e.g., porosity versus acoustic impedance for each lithofacies class separately. Once a significant match between the measured and the predicted reservoir property is attained in the inversion workflow, the petrophysical seismic inversion based on lithofacies classification is applied to the inverted elastic property, i.e., acoustic impedance or Vp/Vs ratio derived from seismic elastic inversion to predict the reservoir properties between the wells. Comparison with the neural network method demonstrated this application of petrophysical seismic inversion to be competitive and reliable.

scholarly journals Stochastic modelling of spatial variability of petrophysical properties in parts of the Niger Delta Basin, southern Nigeria

2019 ◽  
Vol 10 (2) ◽  
pp. 569-585 ◽  
Author(s):  
Ebong D. Ebong ◽  
Anthony E. Akpan ◽  
Stephen E. Ekwok
Keyword(s):  
Spatial Variability ◽  
Niger Delta ◽  
Water Saturation ◽  
Stochastic Modelling ◽  
Sequential Gaussian Simulation ◽  
Stochastic Methods ◽  
Petrophysical Properties ◽  
Reservoir Properties ◽  
Petrophysical Property ◽  
Niger Delta Basin

Abstract Three-dimensional models of petrophysical properties were constructed using stochastic methods to reduce ambiguities associated with estimates for which data is limited to well locations alone. The aim of this study is to define accurate and efficient petrophysical property models that best characterize reservoirs in the Niger Delta Basin at well locations and predicting their spatial continuities elsewhere within the field. Seismic data and well log data were employed in this study. Petrophysical properties estimated for both reservoirs range between 0.15 and 0.35 for porosity, 0.27 and 0.30 for water saturation, and 0.10 and 0.25 for shale volume. Variogram modelling and calculations were performed to guide the distribution of petrophysical properties outside wells, hence, extending their spatial variability in all directions. Transformation of pillar grids of reservoir properties using sequential Gaussian simulation with collocated cokriging algorithm yielded equiprobable petrophysical models. Uncertainties in petrophysical property predictions were performed and visualized based on three realizations generated for each property. The results obtained show reliable approximations of the geological continuity of petrophysical property estimates over the entire geospace.

Assessment of Reservoir Properties using Applied Rock Physics & Seismic Inversion for Near-Field Exploration in a Niger Delta Field

2019 ◽  
Author(s):  
Julius Adesun ◽  
Olanike Olajide ◽  
Chidi Ekesiobi ◽  
Abidoun Ogunjobi ◽  
Kehinde Ishola
Keyword(s):  
Niger Delta ◽  
Near Field ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Reservoir Properties

scholarly journals Seismic Velocity Analysis for Improved Geopressure Modelling in Onshore Niger Delta

2019 ◽  
Vol 7 (2) ◽  
pp. 179
Author(s):  
Emmanuel Bassey Umoren ◽  
Etim Daniel Uko ◽  
Iyeneomie Tamunobereton-Ari ◽  
Chigozie Israel-Cookey
Keyword(s):  
High Resolution ◽  
Pore Pressure ◽  
Acoustic Impedance ◽  
Niger Delta ◽  
Seismic Velocity ◽  
Seismic Inversion ◽  
Velocity Analysis ◽  
Pressure Data ◽  
Model Based ◽  
Inversion Technique

In this study, an improved evaluation of pore pressure using a model based seismic inversion technique has been carried out. Across six wells in the Onshore Niger Delta Basin, post stack seismic volume, angle stack gathers, seismic horizons, check shot, wireline logs, drilling and pressure data were analysed and interpreted. The model based inversion technique was applied to improve the seismic resolution as well as derive acoustic impedance using well velocities along with stacking velocities from velocity analysis of the 3D seismic data. Bowers’ Vp-VES coefficients of 7.43 and 0.77 were used to transform the derived seismic acoustic impedance velocity into seismic pore pressure cube. The seismic acoustic impedance interval velocity reveals much of the geology and resulted to a high resolution seismic pore pressure cube when compared at well location with direct pressure data. The Derived Seismic Pore Pressure (DSPP) also revealed that pore pressure and overpressure can reach or exceed 4000 and 1000psi respectively in the field. The results obtained have demonstrated that seismic acoustic impedance volume can offer high resolution seismic pore pressure cube in both time and space.  

scholarly journals Prediction of Reservoir Quality from Log-Core and Seismic Inversion Analysis with an Artificial Neural Network: A Case Study from the Sawan Gas Field, Pakistan

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 486 ◽  
Author(s):  
Zhang Qiang ◽  
Qamar Yasin ◽  
Naser Golsanami ◽  
Qizhen Du
Keyword(s):  
Spatial Distribution ◽  
Seismic Inversion ◽  
Spatial Variations ◽  
Well Logs ◽  
Sequential Gaussian Simulation ◽  
Support Vector ◽  
Reservoir Quality ◽  
Inversion Algorithm ◽  
Reservoir Properties ◽  
Gas Field

This paper presents a novel approach that aims to predict better reservoir quality regions from seismic inversion and spatial distribution of key reservoir properties from well logs. The reliable estimation of lithology and reservoir parameters at sparsely located wells in the Sawan gas field is still a considerable challenge. This is due to three main reasons: (a) the extreme heterogeneity in the depositional environments, (b) sand-shale intercalations, and (c) repetition of textural changes from fine to coarse sandstone and very coarse sandstone in the reservoir units. In this particular study, machine learning (ML) inversion algorithm was selected to predict the spatial variations of acoustic impedance (AI), porosity, and saturation. While trained in a supervised mode, the support vector machine (SVM) inversion algorithm performed effectively in identifying and mapping individual reservoir properties to delineate and quantify fluid-rich zones. Meanwhile, the Sequential Gaussian Simulation (SGS) and Gaussian Indicator Simulation (GIS) algorithms were employed to determine the spatial variability of lithofacies and porosity from well logs and core analyses data. The calibration of the detailed spatial variations from post-stack seismic inversion using SVM and wireline logs data indicated an appropriate agreement, i.e., variations in AI is related to the variations in reservoir facies and parameters. From the current study, it was concluded that in a highly heterogeneous reservoir, the integration of SVM and GIS algorithms is a reliable approach to achieve the best estimation of the spatial distribution of detailed reservoir characteristics. The results obtained in this study would also be helpful to minimize the uncertainty in drilling, production, and injection in the Sawan gas field of Pakistan as well as other reservoirs worldwide with similar geological settings.

scholarly journals Characterization and geostatistical modeling of reservoirs in ‘Falad’ field, Niger Delta, Nigeria

2021 ◽  
Author(s):  
Ayodele O. Falade ◽  
John O. Amigun ◽  
Yousif M. Makeen ◽  
Olatunbosun O. Kafisanwo
Keyword(s):  
Niger Delta ◽  
Structural Model ◽  
Water Saturation ◽  
Sequential Gaussian Simulation ◽  
Surface Generation ◽  
Geostatistical Modeling ◽  
Reservoir Properties ◽  
Field Development ◽  
Net To Gross ◽  
Well Data

AbstractThis research aims at characterizing and modeling delineated reservoirs in ‘Falad’ Field, Niger Delta, Nigeria, to mitigate the challenge caused by the heterogeneous nature of the reservoirs. Seismic and well log data were integrated, and geostatistics was applied to describe the reservoir properties of the interwell spaces within the study area. Four reservoirs, namely RES 1, RES 2, RES 3 and RES 4, were delineated and correlated across four wells. The reservoir properties {lithology, net to gross, porosity, permeability, water saturation} of all the delineated reservoirs mapped were determined, and two reservoirs with the best quality were picked for further analysis (surface generation and modeling) after ranking the reservoirs based on their quality. Structural interpretation of the field was carried, nine faults were mapped (F1—F9), and the fault polygon was generated. The structural model showed the area is structurally controlled with two of the major faults mapped (F1 and F3) oriented in the SW–NE direction while the other one (F4) is oriented in the NW–SE direction. A 3D grid was constructed using the surfaces of the delineated reservoirs and the reservoir properties were distributed stochastically using simple krigging method with sequential Gaussian simulation, sequential indicator simulation and Gaussian random function simulation algorithms. Geostatistical modeling used in this study has been able to give subsurface information in the areas deficient of well data as the estimated reservoir properties gotten from existing wells have been spatially distributed in the study area and will thus aid future field development while also they are used in identifying new prospect by combining property models with structural maps of the area.

Incorporation of geology with rock physics enables subsalt poststack inversion: A case study in the Gulf of Mexico

Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. WB53-WB65 ◽  
Author(s):  
Huyen Bui ◽  
Jennifer Graham ◽  
Shantanu Kumar Singh ◽  
Fred Snyder ◽  
Martiris Smith
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
Low Frequency ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Reservoir Properties ◽  
Blind Test ◽  
Frequency Model ◽  
Layer Sequence

One of the main goals of seismic inversion is to obtain high-resolution relative and absolute impedance for reservoir properties prediction. We aim to study whether the results from seismic inversion of subsalt data are sufficiently robust for reliable reservoir characterization. Approximately [Formula: see text] of poststack, wide-azimuth, anisotropic (vertical transverse isotropic) wave-equation migration seismic data from 50 Outer Continental Shelf blocks in the Green Canyon area of the Gulf of Mexico were inverted in this study. A total of four subsalt wells and four subsalt seismic interpreted horizons were used in the inversion process, and one of the wells was used for a blind test. Our poststack inversion method used an iterative discrete spike inversion method, based on the combination of space-adaptive wavelet processing to invert for relative acoustic impedance. Next, the dips were estimated from seismic data and converted to a horizon-like layer sequence field that was used as one of the inputs into the low-frequency model. The background model was generated by incorporating the well velocities, seismic velocity, seismic interpreted horizons, and the previously derived layer sequence field in the low-frequency model. Then, the relative acoustic impedance volume was scaled by adding the low-frequency model to match the calculated acoustic impedance logs from the wells for absolute acoustic impedance. Finally, the geological information and rock physics data were incorporated into the reservoir properties assessment for sand/shale prediction in two main target reservoirs in the Miocene and Wilcox formations. Overall, the poststack inversion results and the sand/shale prediction showed good ties at the well locations. This was clearly demonstrated in the blind test well. Hence, incorporating rock physics and geology enables poststack inversion in subsalt areas.

scholarly journals Hydrocarbon Reservoir Characterization of ‘UDI’ Field, Western Niger Delta

2020 ◽  
pp. 12-21
Author(s):  
N. E. Osuya ◽  
J. O. Ayorinde
Keyword(s):  
Niger Delta ◽  
Good Control ◽  
Sequential Gaussian Simulation ◽  
Computational Techniques ◽  
High Porosity ◽  
Simulation Algorithm ◽  
Reservoir Properties ◽  
Hydrocarbon Reservoir ◽  
Potential Reservoir ◽  
Net To Gross

The increasing demand for petroleum products has posed a challenge to the search for oil and gas. This search for hydrocarbon has developed due to advances in computational techniques to evaluate the probability of hydrocarbon proneness of a basin, thereby limiting the risk factor associated with hydrocarbon. This study was therefore designed to assess the hydrocarbon potential and generate a static reservoir model of UDI Field, Onshore Niger Delta. Well, the correlation was carried out to establish stratigraphic continuity of the reservoir sand bodies. The identified potential reservoir intervals were tied to the seismic data using available check shot survey data. With a good match achieved, seismic events were interpreted through paying attention to reflection continuity, amplitude and frequency. Interpreted horizons were converted to surfaces using a convergent interpolation algorithm. Faults within the Field showed a dominant East-West trend with two (2) major faults and five (5) minor ones. A Pixel-based facies model was built based on the normal distribution of the upscaled lithofacies log using the Sequential Indicator Simulation algorithm. Petrophysical models were built by constraining the petrophysical logs to the facies models using Sequential Gaussian simulation algorithm.  Four potential reservoir intervals, A100, A125, A150 and A200 were delineated. Average petrophysical parameters were computed for all the four intervals and the results revealed the reservoir intervals to be of good quality. Sand A100 has the highest average porosity value of 29.4%, while Sand A200 has the lowest value of 25.3%. Net-to-gross ratio also follows the pattern of decreasing value with depth. Sand A150 has the highest average gross thickness value, 170.4 m, while Sand A200 has the least thickness of 80.5 m. The net-to-gross ratio preserved the pattern of gross thickness and this resulted in Sand A150 still having the highest Net thickness and Sand A200 having the least Net sand thickness. The relatively large net sand thicknesses, high net-to-gross ratio values and the high porosity values all support the reservoir intervals within UDI Field to be of good quality. Extrapolations of reservoir properties away from good control honored the geological interpretation of reservoir Sand A125 thereby reducing the subsurface reservoir uncertainties. The availability of pressure data of the reservoir will help in establishing whether the reservoir is compartmentalized and hence the model can be updated to accommodate the effect of compartmentalization.

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