Sweet spot identification through seismic inversion and multiattribute transform: A case study of the Niobrara and Codell unconventionals

2019 ◽  
Vol 7 (4) ◽  
pp. T887-T898
Author(s):  
Sheila Harryandi ◽  
Ali Tura
Keyword(s):  
Hydraulic Fracturing ◽  
Development Strategy ◽  
Seismic Inversion ◽  
Sweet Spot ◽  
Hydrocarbon Production ◽  
Facies Model ◽  
Facies Classification ◽  
Tight Reservoir ◽  
Well Data ◽  
Prestack Seismic Inversion

The Niobrara and Codell unconventional tight reservoir play at the Wattenberg field, Colorado, has potentially two billion barrels of oil equivalent, requiring hundreds of wells to access this resource. Due to the formations’ high heterogeneity and variable thicknesses, we model the facies at the well-bore scale and upscale it to the seismic scale to guide the development strategy and evaluate future exploration targets. A facies classification from well data supervises the prestack seismic inversion and multiattribute transformation workflows to build a 3D facies model. The 3D facies model captures the reservoir heterogeneity throughout the study area and suggests the location of sweet spots for future hydraulic fracturing and refracturing. The term “sweet spot” refers to the parts of the rock formation that potentially have the highest hydrocarbon production. The significance of sweet spot identification includes improved recovery from the Niobrara, Codell, and potential deeper intervals for future exploration of drilling targets. Furthermore, sweet spot information also assists in keeping the well in formation, which is difficult to do without the seismic data. Keeping the well in formation is key for the efficiency of hydraulic fracturing operations and improved production.

Application of geostatistical seismic inversion in reservoir characterization of Sapphire gas field, offshore Nile Delta, Egypt

2019 ◽  
Vol 38 (6) ◽  
pp. 474-479
Author(s):  
Mohamed G. El-Behiry ◽  
Said M. Dahroug ◽  
Mohamed Elattar
Keyword(s):  
Reservoir Characterization ◽  
Nile Delta ◽  
Seismic Inversion ◽  
Reservoir Modeling ◽  
Connectivity Analysis ◽  
Gas Field ◽  
Geostatistical Inversion ◽  
Well Data ◽  
Reservoir Connectivity ◽  
Inversion Techniques

Seismic reservoir characterization becomes challenging when reservoir thickness goes beyond the limits of seismic resolution. Geostatistical inversion techniques are being considered to overcome the resolution limitations of conventional inversion methods and to provide an intuitive understanding of subsurface uncertainty. Geostatistical inversion was applied on a highly compartmentalized area of Sapphire gas field, offshore Nile Delta, Egypt, with the aim of understanding the distribution of thin sands and their impact on reservoir connectivity. The integration of high-resolution well data with seismic partial-angle-stack volumes into geostatistical inversion has resulted in multiple elastic property realizations at the desired resolution. The multitude of inverted elastic properties are analyzed to improve reservoir characterization and reflect the inversion nonuniqueness. These property realizations are then classified into facies probability cubes and ranked based on pay sand volumes to quantify the volumetric uncertainty in static reservoir modeling. Stochastic connectivity analysis was also applied on facies models to assess the possible connected volumes. Sand connectivity analysis showed that the connected pay sand volume derived from the posterior mean of property realizations, which is analogous to deterministic inversion, is much smaller than the volumes generated by any high-frequency realization. This observation supports the role of thin interbed reservoirs in facilitating connectivity between the main sand units.

Prestack Seismic Inversion Based on Anisotropic Markov Random Field

2018 ◽  
Vol 56 (2) ◽  
pp. 1069-1079 ◽  
Author(s):  
Qiang Guo ◽  
Hongbing Zhang ◽  
Feilong Han ◽  
Zuoping Shang
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Seismic Inversion ◽  
Markov Random ◽  
Prestack Seismic Inversion

scholarly journals Gas-bearing sands appraisal for Zamzama Gas field in Pakistan through inverted elastic attributes assisted with PNN approximation of petrophysical properties

2021 ◽  
Author(s):  
Zahid U. Khan ◽  
◽  
Mona Lisa ◽  
Muyyassar Hussain ◽  
Syed A. Ahmed ◽  
...  
Keyword(s):  
Elastic Properties ◽  
Clay Content ◽  
Seismic Inversion ◽  
Indus Basin ◽  
Minimal Risk ◽  
Gas Field ◽  
Lower Indus Basin ◽  
Heterogeneity Effects ◽  
Prestack Seismic Inversion ◽  
Gas Bearing

The Pab Formation of Zamzama block, lying in the Lower Indus Basin of Pakistan, is a prominent gas-producing sand reservoir. The optimized production is limited by water encroachment in producing wells, thus it is required to distinguish the gas-sand facies from the remainder of the wet sands and shales for additional drilling zones. An approach is adopted based on a relation between petrophysical and elastic properties to characterize the prospect locations. Petro-elastic models for the identified facies are generated to discriminate lithologies in their elastic ranges. Several elastic properties, including p-impedance (11,600-12,100 m/s*g/cc), s-impedance (7,000-7,330 m/s*g/cc), and Vp/Vs ratio (1.57-1.62), are calculated from the simultaneous prestack seismic inversion, allowing the identification of gas sands in the field. Furthermore, inverted elastic attributes and well-based lithologies are incorporated into the Bayesian framework to evaluate the probability of gas sands. To better determine reservoir quality, bulk volumes of PHIE and clay are estimated using elastic volumes trained on well logs employing Probabilistic Neural Networking (PNN), which effectively handles heterogeneity effects. The results showed that the channelized gas-sands passing through existing well locations exhibited reduced clay content and maximum effective porosities of 9%, confirming the reservoir's good quality. Such approaches can be widely implemented in producing fields to completely assess litho-facies and achieve maximum production with minimal risk.

Three term AVO approximation ofKf-fm-ρand prestack seismic inversion for deep reservoir

2016 ◽  
Author(s):  
Huixin Wang ◽  
Xingyao Yin ◽  
Cao Danping ◽  
Qijie Zhou ◽  
Wenguo Sun
Keyword(s):  
Seismic Inversion ◽  
Deep Reservoir ◽  
Prestack Seismic Inversion

Seismic-to-well tie of a field of the Nigerian Delta

2021 ◽  
Vol 19 (3) ◽  
pp. 125-138
Author(s):  
S. Inichinbia ◽  
A.L. Ahmed
Keyword(s):  
Seismic Data ◽  
Processing Parameters ◽  
Seismic Inversion ◽  
Seismic Wavelet ◽  
Statistical Measures ◽  
Convolution Model ◽  
Data Driven Approach ◽  
Well Data ◽  
Seismic Reflectors ◽  
Matching Techniques

This paper presents a rigorous but pragmatic and data driven approach to the science of making seismic-to-well ties. This pragmatic  approach is consistent with the interpreter’s desire to correlate geology to seismic information by the use of the convolution model,  together with least squares matching techniques and statistical measures of fit and accuracy to match the seismic data to the well data. Three wells available on the field provided a chance to estimate the wavelet (both in terms of shape and timing) directly from the seismic and also to ascertain the level of confidence that should be placed in the wavelet. The reflections were interpreted clearly as hard sand at H1000 and soft sand at H4000. A synthetic seismogram was constructed and matched to a real seismic trace and features from the well are correlated to the seismic data. The prime concept in constructing the synthetic is the convolution model, which represents a seismic reflection signal as a sequence of interfering reflection pulses of different amplitudes and polarity but all of the same shape. This pulse shape is the seismic wavelet which is formally, the reflection waveform returned by an isolated reflector of unit strength at the target  depth. The wavelets are near zero phase. The goal and the idea behind these seismic-to-well ties was to obtain information on the sediments, calibration of seismic processing parameters, correlation of formation tops and seismic reflectors, and the derivation of a  wavelet for seismic inversion among others. Three seismic-to-well ties were done using three partial angle stacks and basically two formation tops were correlated. Keywords: seismic, well logs, tie, synthetics, angle stacks, correlation,

Application of Machine Learniing For Reservoir Facies Classification in Port Field, Offshore Niger Delta

2021 ◽  
Author(s):  
Jerome Asedegbega ◽  
Oladayo Ayinde ◽  
Alexander Nwakanma
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Niger Delta ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Data Preparation ◽  
K Nearest Neighbor ◽  
Blind Test ◽  
Facies Classification ◽  
Well Data

Abstract Several computer-aided techniques have been developed in recent past to improve interpretational accuracy of subsurface geology. This paradigm shift has provided tremendous success in variety of Machine Learning Application domains and help for better feasibility study in reservoir evaluation using multiple classification techniques. Facies classification is an essential subsurface exploration task as sedimentary facies reflect associated physical, chemical, and biological conditions that formation unit experienced during sedimentation activity. This study however, employed formation samples for facies classification using Machine Learning (ML) techniques and classified different facies from well logs in seven (7) wells of the PORT Field, Offshore Niger Delta. Six wells were concatenated during data preparation and trained using supervised ML algorithms before validating the models by blind testing on one well log to predict discrete facies groups. The analysis started with data preparation and examination where various features of the available well data were conditioned. For the model building and performance, support vector machine, random forest, decision tree, extra tree, neural network (multilayer preceptor), k-nearest neighbor and logistic regression model were built after dividing the data sets into training, test, and blind test well data. Results of metric score for the blind test well estimated for the various models using Jaccard index and F1-score indicated 0.73 and 0.82 for support vector machine, 0.38 and 0.54 for random forest, 0.78 and 0.83 for extra tree, 0.91 and 0.95 for k-nearest neighbor, 0.41 and 0.56 for decision tree, 0.63 and 0.74 for logistic regression, 0.55 and 0.68 for neural network, respectively. The efficiency of ML techniques for enhancing the prediction accuracy and decreasing the procedure time and their approach toward the data, makes it importantly desirable to recommend them in subsurface facies classification analysis.

Prestack Seismic Inversion for Sw Modeling

2005 ◽  
Author(s):  
J. Martin ◽  
B. Bankhead ◽  
A. Sena ◽  
R.S. Cardenas Hernandez ◽  
K.G. Rosas Lara ◽  
...  
Keyword(s):  
Seismic Inversion ◽  
Prestack Seismic Inversion

Applications of quantitative prestack seismic analysis to unconventional resource play characterization in the Permian/Delaware Basin

2019 ◽  
Vol 38 (2) ◽  
pp. 106-115 ◽  
Author(s):  
Phuong Hoang ◽  
Arcangelo Sena ◽  
Benjamin Lascaud
Keyword(s):  
Hydraulic Fracturing ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Seismic Analysis ◽  
Seismic Inversion ◽  
Reservoir Properties ◽  
Yield Attributes ◽  
Horizontal Drilling ◽  
Delaware Basin ◽  
Unconventional Resource

The characterization of shale plays involves an understanding of tectonic history, geologic settings, reservoir properties, and the in-situ stresses of the potential producing zones in the subsurface. The associated hydrocarbons are generally recovered by horizontal drilling and hydraulic fracturing. Historically, seismic data have been used mainly for structural interpretation of the shale reservoirs. A primary benefit of surface seismic has been the ability to locate and avoid drilling into shallow carbonate karsting zones, salt structures, and basement-related major faults which adversely affect the ability to drill and complete the well effectively. More recent advances in prestack seismic data analysis yield attributes that appear to be correlated to formation lithology, rock strength, and stress fields. From these, we may infer preferential drilling locations or sweet spots. Knowledge and proper utilization of these attributes may prove valuable in the optimization of drilling and completion activities. In recent years, geophysical data have played an increasing role in supporting well planning, hydraulic fracturing, well stacking, and spacing. We have implemented an integrated workflow combining prestack seismic inversion and multiattribute analysis, microseismic data, well-log data, and geologic modeling to demonstrate key applications of quantitative seismic analysis utilized in developing ConocoPhillips' acreage in the Delaware Basin located in Texas. These applications range from reservoir characterization to well planning/execution, stacking/spacing optimization, and saltwater disposal. We show that multidisciplinary technology integration is the key for success in unconventional play exploration and development.

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