Seismic-petrophysical reservoir characterization in the northern part of the Chicontepec Basin, Mexico

2016 ◽  
Vol 4 (3) ◽  
pp. T403-T417 ◽  
Author(s):  
Supratik Sarkar ◽  
Sumit Verma ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Seismic Survey ◽  
Rock Properties ◽  
Well Log ◽  
Third Order ◽  
East Central ◽  
Well Control ◽  
Seismic Geomorphology ◽  
Tight Reservoirs

The Chicontepec Formation in east-central Mexico is comprised of complex unconventional reservoirs consisting of low-permeability disconnected turbidite reservoir facies. Hydraulic fracturing increases permeability and joins these otherwise tight reservoirs. We use a recently acquired 3D seismic survey and well control to divide the Chicontepec reservoir interval in the northern part of the basin into five stratigraphic units, equivalent to global third-order seismic sequences. By combining well-log and core information with principles of seismic geomorphology, we are able to map deepwater facies within these stratigraphic units that resulted from the complex interaction of flows from different directions. Correlating these stratigraphic units to producing and nonproducing wells provides the link between rock properties and Chicontepec reservoirs that could be delineated from surface seismic data. The final product is a prestack inversion-driven map of stacked pay that correlates to currently producing wells and indicates potential untapped targets.

Creating detailed subsurface models using predictive image-guided well-log interpolation

2017 ◽  
Vol 5 (3) ◽  
pp. T279-T285 ◽  
Author(s):  
Parvaneh Karimi ◽  
Sergey Fomel ◽  
Rui Zhang
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Structural Information ◽  
Rock Properties ◽  
Well Log ◽  
Initial Model ◽  
The North ◽  
Structural Framework ◽  
Seismic Image ◽  
Seismic Reflectors

Integration of well-log data and seismic data to predict rock properties is an essential but challenging task in reservoir characterization. The standard methods commonly used to create subsurface model do not fully honor the importance of seismic reflectors and detailed structural information in guiding the spatial distribution of rock properties in the presence of complex structures, which can make these methods inaccurate. To overcome initial model accuracy limitations in structurally complex regimes, we have developed a method that uses the seismic image structures to accurately constrain the interpolation of well properties between well locations. A geologically consistent framework provides a more robust initial model that, when inverted with seismic data, delivers a highly detailed yet accurate subsurface model. An application to field data from the North Sea demonstrates the effectiveness of our method, which proves that incorporating the seismic structural framework when interpolating rock properties between wells culminates in the increased accuracy of the final inverted result compared with the standard inversion workflows.

scholarly journals Estimation of reservoir properties from seismic data by smooth neural networks

Geophysics ◽  
2003 ◽  
Vol 68 (6) ◽  
pp. 1969-1983 ◽  
Author(s):  
M. M. Saggaf ◽  
M. Nafi Toksöz ◽  
H. M. Mustafa
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Back Propagation ◽  
Reservoir Rock ◽  
Seismic Survey ◽  
Rock Properties ◽  
Reservoir Properties ◽  
Network Parameters ◽  
Generalization Problem ◽  
Validation Tests

The performance of traditional back‐propagation networks for reservoir characterization in production settings has been inconsistent due to their nonmonotonous generalization, which necessitates extensive tweaking of their parameters in order to achieve satisfactory results and avoid overfitting the data. This makes the accuracy of these networks sensitive to the selection of the network parameters. We present an approach to estimate the reservoir rock properties from seismic data through the use of regularized back propagation networks that have inherent smoothness characteristics. This approach alleviates the nonmonotonous generalization problem associated with traditional networks and helps to avoid overfitting the data. We apply the approach to a 3D seismic survey in the Shedgum area of Ghawar field, Saudi Arabia, to estimate the reservoir porosity distribution of the Arab‐D zone, and we contrast the accuracy of our approach with that of traditional back‐propagation networks through cross‐validation tests. The results of these tests indicate that the accuracy of our approach remains consistent as the network parameters are varied, whereas that of the traditional network deteriorates as soon as deviations from the optimal parameters occur. The approach we present thus leads to more robust estimates of the reservoir properties and requires little or no tweaking of the network parameters to achieve optimal results.

Seismic reservoir characterization of a class-1 amplitude variation with offset turbiditic system located offshore Cote d’Ivoire, West Africa

2018 ◽  
Vol 6 (2) ◽  
pp. SD115-SD128
Author(s):  
Pedro Alvarez ◽  
William Marin ◽  
Juan Berrizbeitia ◽  
Paola Newton ◽  
Michael Barrett ◽  
...  
Keyword(s):  
West Africa ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Rock Properties ◽  
Well Log ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Fluid Content ◽  
Class 1

We have evaluated a case study, in which a class-1 amplitude variation with offset (AVO) turbiditic system located offshore Cote d’Ivoire, West Africa, is characterized in terms of rock properties (lithology, porosity, and fluid content) and stratigraphic elements using well-log and prestack seismic data. The methodology applied involves (1) the conditioning and modeling of well-log data to several plausible geologic scenarios at the prospect location, (2) the conditioning and inversion of prestack seismic data for P- and S-wave impedance estimation, and (3) the quantitative estimation of rock property volumes and their geologic interpretation. The approaches used for the quantitative interpretation of these rock properties were the multiattribute rotation scheme for lithology and porosity characterization and a Bayesian lithofluid facies classification (statistical rock physics) for a probabilistic evaluation of fluid content. The result indicates how the application and integration of these different AVO- and rock-physics-based reservoir characterization workflows help us to understand key geologic stratigraphic elements of the architecture of the turbidite system and its static petrophysical characteristics (e.g., lithology, porosity, and net sand thickness). Furthermore, we found out how to quantify and interpret the risk related to the probability of finding hydrocarbon in a class-1 AVO setting using seismically derived elastic attributes, which are characterized by having a small level of sensitivity to changes in fluid saturation.

THE YELLOWTAIL OIL DISCOVERY

1983 ◽  
Vol 23 (1) ◽  
pp. 170
Author(s):  
A. R. Limbert ◽  
P. N. Glenton ◽  
J. Volaric
Keyword(s):  
Oil Recovery ◽  
Seismic Data ◽  
Joint Venture ◽  
Seismic Survey ◽  
3D Seismic ◽  
Well Control ◽  
Reservoir Volume ◽  
3D Data ◽  
Small Feature ◽  
Gippsland Basin

The Esso/Hematite Yellowtall oil discovery is located about 80 km offshore in the Gippsland Basin. It is a small accumulation situated between the Mackerel and Kingfish oilfields. The oil is contained in Paleocene Latrobe Group sandstones, and sealed by the calcareous shales and siltstones of the Oligocene to Miocene Lakes Entrance Formation. Structural movement and erosion have combined to produce a low relief closure on the unconformity surface at the top of the Latrobe Group.The discovery well, Yellowtail-1, was the culmination of an exploration programme initiated during the early 1970's. The early work involved the recording and interpretation of conventional seismic data and resulted in the drilling of Opah- 1 in 1977. Opah-1 failed to intersect reservoir- quality sediments within the interpreted limits of closure although oil indications were encountered in a non-net interval immediately below the top of the Latrobe Group. In 1980 the South Mackerel 3D seismic survey was recorded. The interpretation of these 3D data in conjunction with the existing well control resulted in the drilling of Yellowtail-1 and subsequently led to the drilling of Yellowtail-2.In spite of the intensive exploration to which this small feature has been subjected, the potential for its development remains uncertain. Technical factors which affect the viability of a Yellowtail development are:The low relief of the closure makes the reservoir volume highly sensitive to depth conversion of the seismic data.The complicated velocity field makes precise depth conversion difficult.The thin oil column reduces oil recovery efficiency.The detailed pattern of erosion at the top of the Latrobe Group may be beyond the resolution capability of 3D seismic data.The 3D seismic data may not be capable of defining the distribution of the non-net intervals within the trap.The large anticlinal closures and topographic highs in the Gippsland Basin have been drilled, and the prospects that remain are generally small or high risk. Such exploration demands higher technology in the exploration stage and more wells to define the discoveries, and has no guarantee of success. The Yellowtail discovery is an illustration of one such prospect that the Esso/Hematite joint venture is evaluating.

THE EASTERN OTWAY BASIN WANGERRIP GROUP REVISITED USING AN INTEGRATED SEQUENCE STRATIGRAPHIC METHODOLOGY

1995 ◽  
Vol 35 (1) ◽  
pp. 372 ◽  
Author(s):  
P. A. Arditto
Keyword(s):  
Seismic Data ◽  
Well Log ◽  
Third Order ◽  
Traditional Methods ◽  
Depositional History ◽  
Sequence Stratigraphic ◽  
Reflection Seismic ◽  
Stratigraphic Subdivision ◽  
History Of ◽  
Definition Of

Recent exploration by BHP Petroleum in VIC/ P30 and VIC/P31, within the eastern Otway Basin, has contributed significantly to our understanding of the depositional history of the Paleocene to Eocene siliciclastic Wangerrip Group. The original lithostratigraphic definition of this group was based on outcrop description and subsequently applied to onshore and, more recently, offshore wells significantly basinward of the type sections. This resulted in confusing individual well lithostratigraphies which hampered traditional methods of subsurface correlation.A re-evaluation of the Wangerrip Group stratigraphy is presented based on the integration of outcrop, wireline well log, palynological and reflection seismic data. The Wangerrip Group can be divided into two distinct units based on seismic and well log character. A lower Paleocene succession rests conformably on the underlying Maastrichtian and older Sherbrook Group, and is separated from an overlying Late Paleocene to Eocene succession by a significant regional unconformity. This upper unit displays a highly progradational seismic character and is named here as the Wangerrip Megasequence.Regional seismic and well log correlation diagrams are used to illustrate a subdivision of the Wangerrip Megasequence into eight third-order sequences. This sequence stratigraphic subdivision of the Wangerrip Group is then used to construct a chronostratigraphic chart for the succession within this part of the Otway Basin.

Seismic reservoir characterization of the Bone Spring and Wolfcamp Formations in the Delaware Basin: Challenges and uncertainty in characterization using rock physics — A case study: Part 2

2020 ◽  
Vol 8 (4) ◽  
pp. T1057-T1069
Author(s):  
Ritesh Kumar Sharma ◽  
Satinder Chopra ◽  
Larry Lines
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Well Log ◽  
Data Set ◽  
Frequency Model ◽  
Log Data ◽  
Delaware Basin ◽  
Seismic Reservoir

The discrimination of fluid content and lithology in a reservoir is important because it has a bearing on reservoir development and its management. Among other things, rock-physics analysis is usually carried out to distinguish between the lithology and fluid components of a reservoir by way of estimating the volume of clay, water saturation, and porosity using seismic data. Although these rock-physics parameters are easy to compute for conventional plays, there are many uncertainties in their estimation for unconventional plays, especially where multiple zones need to be characterized simultaneously. We have evaluated such uncertainties with reference to a data set from the Delaware Basin where the Bone Spring, Wolfcamp, Barnett, and Mississippian Formations are the prospective zones. Attempts at seismic reservoir characterization of these formations have been developed in Part 1 of this paper, where the geologic background of the area of study, the preconditioning of prestack seismic data, well-log correlation, accounting for the temporal and lateral variation in the seismic wavelets, and building of robust low-frequency model for prestack simultaneous impedance inversion were determined. We determine the challenges and the uncertainty in the characterization of the Bone Spring, Wolfcamp, Barnett, and Mississippian sections and explain how we overcame those. In the light of these uncertainties, we decide that any deterministic approach for characterization of the target formations of interest may not be appropriate and we build a case for adopting a robust statistical approach. Making use of neutron porosity and density porosity well-log data in the formations of interest, we determine how the type of shale, volume of shale, effective porosity, and lithoclassification can be carried out. Using the available log data, multimineral analysis was also carried out using a nonlinear optimization approach, which lent support to our facies classification. We then extend this exercise to derived seismic attributes for determination of the lithofacies volumes and their probabilities, together with their correlations with the facies information derived from mud log data.

Accelerating seismic fault and stratigraphy interpretation with deep CNNs: A case study of the Taranaki Basin, New Zealand

2020 ◽  
Vol 39 (10) ◽  
pp. 727-733
Author(s):  
Haibin Di ◽  
Leigh Truelove ◽  
Cen Li ◽  
Aria Abubakar
Keyword(s):  
New Zealand ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Seismic Survey ◽  
Deep Convolutional Neural Networks ◽  
Seismic Attribute ◽  
Data Set ◽  
Seismic Fault ◽  
Attribute Analysis ◽  
Specific Implementation

Accurate mapping of structural faults and stratigraphic sequences is essential to the success of subsurface interpretation, geologic modeling, reservoir characterization, stress history analysis, and resource recovery estimation. In the past decades, manual interpretation assisted by computational tools — i.e., seismic attribute analysis — has been commonly used to deliver the most reliable seismic interpretation. Because of the dramatic increase in seismic data size, the efficiency of this process is challenged. The process has also become overly time-intensive and subject to bias from seismic interpreters. In this study, we implement deep convolutional neural networks (CNNs) for automating the interpretation of faults and stratigraphies on the Opunake-3D seismic data set over the Taranaki Basin of New Zealand. In general, both the fault and stratigraphy interpretation are formulated as problems of image segmentation, and each workflow integrates two deep CNNs. Their specific implementation varies in the following three aspects. First, the fault detection is binary, whereas the stratigraphy interpretation targets multiple classes depending on the sequences of interest to seismic interpreters. Second, while the fault CNN utilizes only the seismic amplitude for its learning, the stratigraphy CNN additionally utilizes the fault probability to serve as a structural constraint on the near-fault zones. Third and more innovatively, for enhancing the lateral consistency and reducing artifacts of machine prediction, the fault workflow incorporates a component of horizontal fault grouping, while the stratigraphy workflow incorporates a component of feature self-learning of a seismic data set. With seven of 765 inlines and 23 of 2233 crosslines manually annotated, which is only about 1% of the available seismic data, the fault and four sequences are well interpreted throughout the entire seismic survey. The results not only match the seismic images, but more importantly they support the graben structure as documented in the Taranaki Basin.

Fluids and lithofacies prediction based on integration of well-log data and seismic inversion: a machine learning approach

Geophysics ◽  
2021 ◽  
pp. 1-67
Author(s):  
Luanxiao Zhao ◽  
Caifeng Zou ◽  
Yuanyuan Chen ◽  
Wenlong Shen ◽  
Yirong Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Domain Knowledge ◽  
Reservoir Characterization ◽  
Model Building ◽  
Imbalanced Data ◽  
Seismic Inversion ◽  
Well Log ◽  
Spatial Constraints ◽  
Imbalanced Data Sets

Seismic prediction of fluid and lithofacies distributions is of great interest to reservoir characterization, geological model building, and flow unit delineation. Inferring fluids and lithofacies from seismic data under the framework of machine learning is commonly subject to issues of limited features, imbalanced data sets, and spatial constraints. As a consequence, an XGBoost based workflow, which takes feature engineering, data balancing, and spatial constraints into account, is proposed to predict the fluid and lithofacies distribution by integrating well-log and seismic data. The constructed feature set based on simple mathematical operations and domain knowledge outperforms the benchmark group consisting of conventional elastic attributes of P-impedance and Vp/Vs ratio. A radial basis function characterizing the weights of training samples according to the distances from the available wells to the target region is developed to impose spatial constraints on the model training process, significantly improving the prediction accuracy and reliability of gas sandstone. The strategy combining the synthetic minority oversampling technique (SMOTE) and spatial constraints further increases the F1 score of gas sandstone and also benefits the overall prediction performance of all the facies. The application of the combined strategy on prestack seismic inversion results generates a more geologically reasonable spatial distribution of fluids, thus verifying the robustness and effectiveness of the proposed workflow.

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