3D Geological Model Creates Potential for Increased Production in Libyan Field

2021 ◽  
Vol 73 (08) ◽  
pp. 44-45
Author(s):  
Chris Carpenter
Keyword(s):  
Upper Cretaceous ◽  
Reservoir Characterization ◽  
Technical Conference ◽  
Reservoir Quality ◽  
Geostatistical Model ◽  
Reservoir Properties ◽  
Hydrocarbon Production ◽  
Reservoir Characteristics ◽  
Sirte Basin ◽  
Oil Company

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 201417, “Reservoir Characterization and Geostatistical Model of the Cretaceous and Cambrian-Ordovician Reservoir Intervals, Meghil Field, Sirte Basin, Libya,” by Mohamed Masoud, Sirte Oil Company; W. Scott Meddaugh, SPE, Midwestern State University; and Masud Eljaroshi Masud, Sirte Oil Company, prepared for the 2020 SPE Annual Technical Conference and Exhibition, originally scheduled to be held in Denver, Colorado, 5–7 October. The paper has not been peer reviewed. The study outlined in the complete paper focuses on developing models of the Upper Cretaceous Waha carbonate and Bahi sandstone reservoirs and the Cambrian-Ordovician Gargaf sandstone reservoir in the Meghil field, Sirte Basin, Libya. The objective of this study is to develop a representative geostatistically based 3D model that preserves geological elements and eliminates uncertainty of reservoir properties and volumetric estimates. This study demonstrates the potential for significant additional hydrocarbon production from the Meghil field and the effect of heterogeneity on well placement and spacing. Introduction The reservoir of interest consists of three stratigraphic layers of different ages: the Waha and Bahi Formations and the Gargaf Group intersecting the Meghil field. The Waha reservoir is a porous limestone that forms a single reservoir with underlying Upper Cretaceous Bahi sandstone and Cambro-Ordovician Gargaf Group quartzitic sandstone. The Waha provides excel-lent reservoir characteristics. The Bahi has fair to good reservoir characteristics, while the Gargaf Group has very poor reservoir quality. The Waha and Bahi contain significant amounts of hydrocarbons. The Bahi is composed of erratically distributed detritus from the eroded Gargaf Group. The characteristic of the Gargaf sediments is quartzitic sandstones indurate to a quartzite with low reservoir quality.

Factors Controlling Porosity Permeability Relationship for Reservoir Quality Prediction: A Case Study of Malay Basin Reservoir, Malaysia

2021 ◽  
Author(s):  
Fadzlin Hasani Kasim ◽  
Budi Priyatna Kantaatmadja ◽  
Wan Nur Wan M Zainudin ◽  
Amita Ali ◽  
Hasnol Hady Ismail ◽  
...  
Keyword(s):  
Reservoir Characterization ◽  
Visual Analysis ◽  
Mineral Dissolution ◽  
Rock Properties ◽  
Reservoir Quality ◽  
Hydrocarbon Potential ◽  
Lower Grade ◽  
Reservoir Properties ◽  
Reservoir Evaluation ◽  
Porosity And Permeability

Abstract Predicting the spatial distribution of rock properties is the key to a successful reservoir evaluation for hydrocarbon potential. However, a reservoir with a complex environmental setting (e.g. shallow marine) becomes more challenging to be characterized due to variations of clay, grain size, compaction, cementation, and other diagenetic effects. The assumption of increasing permeability value with an increase of porosity may not be always the case in such an environment. This study aims to investigate factors controlling the porosity and permeability relationships at Lower J Reservoir of J20, J25, and J30, Malay Basin. Porosity permeability values from routine core analysis were plotted accordingly in four different sets which are: lithofacies based, stratigraphic members based, quartz volume-based, and grain-sized based, to investigate the trend in relating porosity and permeability distribution. Based on petrographical studies, the effect of grain sorting, mineral type, and diagenetic event on reservoir properties was investigated and characterized. The clay type and its morphology were analyzed using X-ray Diffractometer (XRD) and Spectral electron microscopy. Results from porosity and permeability cross-plot show that lithofacies type play a significant control on reservoir quality. It shows that most of the S1 and S2 located at top of the plot while lower grade lithofacies of S41, S42, and S43 distributed at the middle and lower zone of the plot. However, there are certain points of best and lower quality lithofacies not located in the theoretical area. The detailed analysis of petrographic studies shows that the diagenetic effect of cementation and clay coating destroys porosity while mineral dissolution improved porosity. A porosity permeability plot based on stratigraphic members showed that J20 points located at the top indicating less compaction effect to reservoir properties. J25 and J30 points were observed randomly distributed located at the middle and bottom zone suggesting that compaction has less effect on both J25 and J30 sands. Lithofacies description that was done by visual analysis through cores only may not correlate-able with rock properties. This is possibly due to the diagenetic effect which controls porosity and permeability cannot visually be seen at the core. By incorporating petrographical analysis results, the relationship between porosity, permeability, and lithofacies can be further improved for better reservoir characterization. The study might change the conventional concept that lower quality lithofacies does not have economic hydrocarbon potential and unlock more hydrocarbon-bearing reserves especially in these types of environmental settings.

scholarly journals Evaluation of Reservoir Quality and Forecasted Production Variability along a Multi-Fractured Horizontal Well. Part 1: Reservoir Characterization

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6154
Author(s):  
Daniela Becerra ◽  
Christopher R. Clarkson ◽  
Amin Ghanizadeh ◽  
Rafael Pires de Lima ◽  
Farshad Tabasinejad ◽  
...  
Keyword(s):  
Horizontal Well ◽  
Reservoir Characterization ◽  
Water Saturation ◽  
Geometric Approach ◽  
Horizontal Wells ◽  
Reservoir Quality ◽  
Reservoir Properties ◽  
Geomechanical Properties ◽  
Using Data ◽  
Lateral Length

Completion design for horizontal wells is typically performed using a geometric approach where the fracturing stages are evenly distributed along the lateral length of the well. However, this approach ignores the intrinsic vertical and horizontal heterogeneity of unconventional reservoirs, resulting in uneven production from hydraulic fracturing stages. An alternative approach is to selectively complete intervals with similar and superior reservoir quality (RQ) and completion quality (CQ), potentially leading to improved development efficiency. In the current study, along-well reservoir characterization is performed using data from a horizontal well completed in the Montney Formation in western Canada. Log-derived petrophysical and geomechanical properties, and laboratory analyses performed on drill cuttings, are integrated for the purpose of evaluating RQ and CQ variability along the well. For RQ, cutoffs were applied to the porosity (>4%), permeability (>0.0018 mD), and water saturation (<20%), whereas, for CQ, cutoffs were applied to rock strength (<160 Mpa), Young’s Modulus (60–65 GPa), and Poisson’s ratio (<0.26). Based on the observed heterogeneity in reservoir properties, the lateral length of the well can be subdivided into nine segments. Superior RQ and CQ intervals were found to be associated with predominantly (massive) porous siltstone facies; these intervals are regarded as the primary targets for stimulation. In contrast, relatively inferior RQ and CQ intervals were found to be associated with either dolomite-cemented facies or laminated siltstones. The methods developed and used in this study could be beneficial to Montney operators who aim to better predict and target sweet spots along horizontal wells; the approach could also be used in other unconventional plays.

Integrated geologic, geophysical, and petrophysical data to construct full field geologic model of Cambrian-Ordovician and Upper Cretaceous reservoir formations, Central Western Sirte Basin, Libya

2019 ◽  
Vol 7 (1) ◽  
pp. T21-T37
Author(s):  
Abdalla A. Abdelnabi ◽  
Yousf Abushalah ◽  
Kelly H. Liu ◽  
Stephen S. Gao
Keyword(s):  
Upper Cretaceous ◽  
Thickness Variation ◽  
Reservoir Properties ◽  
Full Field ◽  
Vertical Heterogeneity ◽  
Field Static ◽  
Original Oil In Place ◽  
Well Data ◽  
Sirte Basin

The Cambrian-Ordovician and Upper Cretaceous formations, which are the main oil-producing formations in the central Sirte Basin, are structurally complex. The lateral and vertical heterogeneity of the reservoir formations is not well-understood, which negatively affects the performance of the reservoirs. We constructed efficient full-field static models that incorporate the lateral and vertical variation of those reservoir formations by integrating geologic and geophysical data. We determined lithology and reservoir properties by selecting appropriate petrophysical techniques that suit the available well data and overcome issues with unreliable well-log measurements. In the process of building structural models, defining and mapping the base of the Cambrian-Ordovician Gargaf Formation was very challenging because wells did not penetrate the basal formation, and the quality of the seismic data decreases with depth. Therefore, we applied techniques of adding isochore maps of the overlying Upper Cretaceous of the Bahi and Waha Formations to map basal contact and determine the thickness of the Gargaf Formation for the first time in the area. The constructed isochore maps showed the thickness variation and the distributions of the Bahi and Waha Formations and explained the influence of Gargaf paleotopography and faults on them. The fault models combined with facies and property models suggested an interconnection among the three main reservoirs. They also indicated that the quality of the Waha reservoir enhances as the lithology varies from limestones to calcareous sandstones, whereas the quality of the Gargaf reservoir was primarily controlled by fractures. The total estimate of the original oil in place with the largest contribution of hydrocarbon volume from the Waha Formation was [Formula: see text] stock tank barrel. The created model with a fine-scale geocellular covering an area of [Formula: see text] is unique to the study area and it can be updated and refined at any time with new data production and drilling activities.

Frequency-dependent AVO analysis using the scattering response of a layered reservoir

Geophysics ◽  
2020 ◽  
Vol 85 (2) ◽  
pp. N1-N16
Author(s):  
Dhananjay Kumar ◽  
Zeyu Zhao ◽  
Douglas J. Foster ◽  
Danica Dralus ◽  
Mrinal K. Sen
Keyword(s):  
Reservoir Characterization ◽  
Seismic Analysis ◽  
Maximum Amplitude ◽  
Reservoir Quality ◽  
Seismic Velocities ◽  
Reservoir Properties ◽  
Frequency Dependent ◽  
Seismic Scattering ◽  
Gas Saturation ◽  
Avo Attributes

Sensitivity of reservoir properties to broadband seismic amplitudes can be weak, which makes interpretation ambiguous. Examples of challenging interpretation scenarios include distinguishing blocky reservoirs from fining sequences, low gas saturation from high gas saturation, and variable reservoir quality. Some of these rock and fluid changes might indicate stronger sensitivity to amplitudes over narrow frequency bands, which is a characteristic of frequency-dependent amplitude variation with offset (FAVO). We have developed a FAVO model for reservoir characterization, following a seismic scattering phenomenon through a set of isotropic elastic layers. The frequency dependency in our model comes from the time delays due to wave propagation within layers. The FAVO modeled response is a complex-valued amplitude varying with angle and frequency, and it is a function of the seismic velocities and thicknesses of individual layers, along with the conventional AVO response at all interfaces. Our FAVO seismic analysis consists of two main steps: (1) forward modeling using well logs to understand rock and fluid sensitivity to amplitudes to identify tuning frequencies with maximum amplitude excursions and (2) seismic analysis at tuning frequencies. With well-log models, we observed that the frequency-dependent tuning response is primarily dependent on the lithology stacking pattern of a reservoir; in the cases studied, the fluid and reservoir quality have secondary effects on the frequency dependence of the amplitudes. We evaluate synthetic models and field data from the Columbus Basin, Trinidad, to illustrate our frequency-dependent seismic analysis methods. For one of the sandstone reservoirs, a frequency-dependent attribute indicates better spatial resolution of the anomaly than a conventional amplitude extraction. FAVO attributes are complementary to conventional AVO attributes.

scholarly journals Petrophysical Reservoir Characterization of Habiganj Gas Field, Surma Basin, Bangladesh

2021 ◽  
Vol 10 (1) ◽  
pp. 1-10
Author(s):  
Shamiha Shafinaz Shreya ◽  
Md Anwar Hossain Bhuiyan ◽  
Shakhawat Hossain ◽  
Tania Sultana
Keyword(s):  
Reservoir Characterization ◽  
Water Saturation ◽  
Volumetric Analysis ◽  
Reservoir Quality ◽  
Gas Field ◽  
Hydrocarbon Production ◽  
Petrophysical Parameters ◽  
Shale Volume ◽  
Lower Reservoir ◽  
Surma Basin

The previous studies on the petrophysical and volumetric analysis of Habiganj gas field were based on limited well data. As the accuracy of volumetric analysis relies greatly on petrophysical parameters, it is important to estimate them accurately. In this study we analyzed all eleven wells drilled in the Habiganj field to determine the petrophysical parameters. Analysis of the well logs revealed two distinct reservoir zones in this field termed as upper reservoir zone and lower reservoir zone. Stratigraphically, these two reservoir zones are in the Bokabil and Bhuban Formation of Surma Group. Petrophysical analysis shows significant differences between the two zones in terms of petrophysical parameters. Porosity in the upper reservoir zone ranges from 12% to 36%, with an average of 28%. This zone is highly permeable, as indicated by the average permeability of 500 mili Darcy (mD). The average water saturation in this zone is around 18% suggesting high gas saturation. The lower reservoir zone has an average porosity, permeability, and water saturation of 12%, 60mD, and 43%, respectively, indicating poor reservoir quality. An analysis of log motifs indicates that the upper reservoir zone is composed of stacked sands of blocky pattern. The sands in this interval are clean, as indicated by the lower shale volume of 12-15%. The average thickness of this zone is 230m, and the presence of this zone in all the drilled wells suggests high lateral continuity. The lower reservoir zone consists of sand bodies of serrated pattern. The sands have high shale volume and are laterally discontinuous. Overall, the upper reservoir zone has superior petrophysical properties to the lower reservoir zone. Although the reservoir quality of the lower reservoir zone is poorer than that of the upper zone, this zone can be considered as the secondary target for hydrocarbon production. Petrophysical parameters of this study were estimated from all the eleven wells drilled in this field; hence the values are more accurate. The reported values of the petrophysical parameters in this study are recommended to use to re-estimate the reserves in Habiganj field. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 10(1), 2021, P 1-10

Latest developments in seismic texture analysis for subsurface structure, facies, and reservoir characterization: A review

Geophysics ◽  
2011 ◽  
Vol 76 (2) ◽  
pp. W1-W13 ◽  
Author(s):  
Dengliang Gao
Keyword(s):  
Texture Analysis ◽  
Reservoir Characterization ◽  
Preliminary Test ◽  
Seismic Facies ◽  
Least Squares Regression ◽  
Reservoir Properties ◽  
Seismic Amplitude ◽  
Texture Model ◽  
Facies Classification ◽  
Analysis Window

In exploration geology and geophysics, seismic texture is still a developing concept that has not been sufficiently known, although quite a number of different algorithms have been published in the literature. This paper provides a review of the seismic texture concepts and methodologies, focusing on latest developments in seismic amplitude texture analysis, with particular reference to the gray level co-occurrence matrix (GLCM) and the texture model regression (TMR) methods. The GLCM method evaluates spatial arrangements of amplitude samples within an analysis window using a matrix (a two-dimensional histogram) of amplitude co-occurrence. The matrix is then transformed into a suite of texture attributes, such as homogeneity, contrast, and randomness, which provide the basis for seismic facies classification. The TMR method uses a texture model as reference to discriminate among seismic features based on a linear, least-squares regression analysis between the model and the data within an analysis window. By implementing customized texture model schemes, the TMR algorithm has the flexibility to characterize subsurface geology for different purposes. A texture model with a constant phase is effective at enhancing the visibility of seismic structural fabrics, a texture model with a variable phase is helpful for visualizing seismic facies, and a texture model with variable amplitude, frequency, and size is instrumental in calibrating seismic to reservoir properties. Preliminary test case studies in the very recent past have indicated that the latest developments in seismic texture analysis have added to the existing amplitude interpretation theories and methodologies. These and future developments in seismic texture theory and methodologies will hopefully lead to a better understanding of the geologic implications of the seismic texture concept and to an improved geologic interpretation of reflection seismic amplitude.

Geostatistical Inversion in Carbonate Reservoirs to Map Reservoir Quality With High Predictability – A Case Study From Onshore Abu Dhabi

2021 ◽  
Author(s):  
S Al Naqbi ◽  
J Ahmed ◽  
J Vargas Rios ◽  
Y Utami ◽  
A Elila ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Pore Volume ◽  
Rock Physics ◽  
Static Model ◽  
Reservoir Quality ◽  
Reservoir Properties ◽  
Geostatistical Inversion ◽  
High Predictability ◽  
The Impact

Abstract The Thamama group of reservoirs consist of porous carbonates laminated with tight carbonates, with pronounced lateral heterogeneities in porosity, permeability, and reservoir thickness. The main objective of our study was mapping variations and reservoir quality prediction away from well control. As the reservoirs were thin and beyond seismic resolution, it was vital that the facies and porosity be mapped in high resolution, with a high predictability, for successful placement of horizontal wells for future development of the field. We established a unified workflow of geostatistical inversion and rock physics to characterize the reservoirs. Geostatistical inversion was run in static models that were converted from depth to time domain. A robust two-way velocity model was built to map the depth grid and its zones on the time seismic data. This ensured correct placement of the predicted high-resolution elastic attributes in the depth static model. Rock physics modeling and Bayesian classification were used to convert the elastic properties into porosity and lithology (static rock-type (SRT)), which were validated in blind wells and used to rank the multiple realizations. In the geostatistical pre-stack inversion, the elastic property prediction was constrained by the seismic data and controlled by variograms, probability distributions and a guide model. The deterministic inversion was used as a guide or prior model and served as a laterally varying mean. Initially, unconstrained inversion was tested by keeping all wells as blind and the predictions were optimized by updating the input parameters. The stochastic inversion results were also frequency filtered in several frequency bands, to understand the impact of seismic data and variograms on the prediction. Finally, 30 wells were used as input, to generate 80 realizations of P-impedance, S-impedance, Vp/Vs, and density. After converting back to depth, 30 additional blind wells were used to validate the predicted porosity, with a high correlation of more than 0.8. The realizations were ranked based on the porosity predictability in blind wells combined with the pore volume histograms. Realizations with high predictability and close to the P10, P50 and P90 cases (of pore volume) were selected for further use. Based on the rock physics analysis, the predicted lithology classes were associated with the geological rock-types (SRT) for incorporation in the static model. The study presents an innovative approach to successfully integrate geostatistical inversion and rock physics with static modeling. This workflow will generate seismically constrained high-resolution reservoir properties for thin reservoirs, such as porosity and lithology, which are seamlessly mapped in the depth domain for optimized development of the field. It will also account for the uncertainties in the reservoir model through the generation of multiple equiprobable realizations or scenarios.

Unsupervised machine learning using 3D seismic data applied to reservoir evaluation and rock type identification

2021 ◽  
pp. 1-69
Author(s):  
Marwa Hussein ◽  
Robert R. Stewart ◽  
Deborah Sacrey ◽  
Jonny Wu ◽  
Rajas Athale
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Rock Type ◽  
Seismic Attributes ◽  
Reservoir Quality ◽  
3D Seismic ◽  
Reservoir Properties ◽  
Unsupervised Machine Learning ◽  
Amplitude Data ◽  
3D Seismic Data

Net reservoir discrimination and rock type identification play vital roles in determining reservoir quality, distribution, and identification of stratigraphic baffles for optimizing drilling plans and economic petroleum recovery. Although it is challenging to discriminate small changes in reservoir properties or identify thin stratigraphic barriers below seismic resolution from conventional seismic amplitude data, we have found that seismic attributes aid in defining the reservoir architecture, properties, and stratigraphic baffles. However, analyzing numerous individual attributes is a time-consuming process and may have limitations for revealing small petrophysical changes within a reservoir. Using the Maui 3D seismic data acquired in offshore Taranaki Basin, New Zealand, we generate typical instantaneous and spectral decomposition seismic attributes that are sensitive to lithologic variations and changes in reservoir properties. Using the most common petrophysical and rock typing classification methods, the rock quality and heterogeneity of the C1 Sand reservoir are studied for four wells located within the 3D seismic volume. We find that integrating the geologic content of a combination of eight spectral instantaneous attribute volumes using an unsupervised machine-learning algorithm (self-organizing maps [SOMs]) results in a classification volume that can highlight reservoir distribution and identify stratigraphic baffles by correlating the SOM clusters with discrete net reservoir and flow-unit logs. We find that SOM classification of natural clusters of multiattribute samples in the attribute space is sensitive to subtle changes within the reservoir’s petrophysical properties. We find that SOM clusters appear to be more sensitive to porosity variations compared with lithologic changes within the reservoir. Thus, this method helps us to understand reservoir quality and heterogeneity in addition to illuminating thin reservoirs and stratigraphic baffles.

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