The value of VSP data through early phases of field appraisal and development: A modeling and acquisition case study in the Gulf of Mexico

2019 ◽  
Vol 38 (11) ◽  
pp. 850-857 ◽  
Author(s):  
Peter Lanzarone ◽  
Elizabeth L'Heureux ◽  
Qingsong Li
Keyword(s):  
Gulf Of Mexico ◽  
Reservoir Characterization ◽  
Velocity Model ◽  
Complex Salt ◽  
Oil Field ◽  
Ultrahigh Pressure ◽  
Vsp Data ◽  
The Impact ◽  
Early Phases

The Gulf of Mexico is a rich hydrocarbon province that contains a diversity of petroleum systems play types. Often, identifying drilling targets can be challenging when solely using surface seismic data, particularly in areas with complex salt structures in the overburden. In this paper, we present a vertical seismic profile (VSP) modeling and acquisition case study for an oil field located in a subsalt, deepwater, ultrahigh-pressure high-temperature environment. Our objective was to model the subsurface to guide the acquisition of VSP data during the early phases of exploration and appraisal drilling. In the first exploration well, a salt-proximity VSP designed in a walkaway configuration was carried out to help better define the geometry of a salt overhang and verify anisotropy parameters, helping to reduce a critical uncertainty for imaging the subsalt structure across a large segment within our field area. In the first appraisal well, a zero-offset VSP was collected to establish a direct well tie and further calibrate our velocity model. In the second appraisal well, we utilized walkaway VSP data to form a high-frequency stratigraphic image between the two appraisal wellbores. These data were used to generate an enhanced image of the reservoir section that revealed subtle stratigraphic boundaries, another key subsurface uncertainty. Finally, we modeled both ambitious and conservative 3D VSP acquisition designs to understand the imaging area achieved through a 3D acquisition and undertook an assessment to understand the impact of PP and PS imaging for reservoir characterization. We conclude that VSP data are valuable tools in the early phases of field appraisal and development, and we demonstrate the business value of VSPs to optimize development drilling locations in our study area.

Applications of Artificial Neural Networks for Seismic Facies Classification

2021 ◽  
Vol 73 (02) ◽  
pp. 68-69
Author(s):  
Chris Carpenter
Keyword(s):  
Neural Network ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Probabilistic Neural Network ◽  
Oil Field ◽  
Seismic Facies ◽  
Data Set ◽  
Full Field ◽  
Facies Classification

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 200577, “Applications of Artificial Neural Networks for Seismic Facies Classification: A Case Study From the Mid-Cretaceous Reservoir in a Supergiant Oil Field,” by Ali Al-Ali, Karl Stephen, SPE, and Asghar Shams, Heriot-Watt University, prepared for the 2020 SPE Europec featured at the 82nd EAGE Conference and Exhibition, originally scheduled to be held in Amsterdam, 1-3 December. The paper has not been peer reviewed. Facies classification using data from sources such as wells and outcrops cannot capture all reservoir characterization in the interwell region. Therefore, as an alternative approach, seismic facies classification schemes are applied to reduce the uncertainties in the reservoir model. In this study, a machine-learning neural network was introduced to predict the lithology required for building a full-field Earth model for carbonate reservoirs in southern Iraq. The work and the methodology provide a significant improvement in facies classification and reveal the capability of a probabilistic neural network technique. Introduction The use of machine learning in seismic facies classification has increased gradually during the past decade in the interpretation of 3D and 4D seismic volumes and reservoir characterization work flows. The complete paper provides a literature review regarding this topic. Previously, seismic reservoir characterization has revealed the heterogeneity of the Mishrif reservoir and its distribution in terms of the pore system and the structural model. However, the main objective of this work is to classify and predict the heterogeneous facies of the carbonate Mishrif reservoir in a giant oil field using a multilayer feed-forward network (MLFN) and a probabilistic neural network (PNN) in nonlinear facies classification techniques. A related objective was to find any domain-specific causal relationships among input and output variables. These two methods have been applied to classify and predict the presence of different facies in Mishrif reservoir rock types. Case Study Reservoir and Data Set Description. The West Qurna field is a giant, multibillion-barrel oil field in the southern Mesopotamian Basin with multiple carbonate and clastic reservoirs. The overall structure of the field is a north/south trending anticline steep on the western flank and gentle on the eastern flank. Many producing reservoirs developed in this oil field; however, the Mid- Cretaceous Mishrif reservoir is the main producing reservoir. The reservoir consists of thick carbonate strata (roughly 250 m) deposited on a shallow water platform adjacent to more-distal, deeper-water nonreservoir carbonate facies developing into three stratigraphic sequence units in the second order. Mishrif facies are characterized by a porosity greater than 20% and large permeability contrast from grainstones to microporosity (10-1000 md). The first full-field 3D seismic data set was achieved over 500 km2 during 2012 and 2013 in order to plan the development of all field reservoirs. A de-tailed description of the reservoir has been determined from well logs and core and seismic data. This study is mainly based on facies log (22 wells) and high-resolution 3D seismic volume to generate seismic attributes as the input data for the training of the neural network model. The model is used to evaluate lithofacies in wells without core data but with appropriate facies logs. Also, testing was carried out in parallel with the core data to verify the results of facies classification.

Crosswell seismic reflection/diffraction tomography: A reservoir characterization application

Geophysics ◽  
1994 ◽  
Vol 59 (3) ◽  
pp. 351-361 ◽  
Author(s):  
M. Ali C. Tura ◽  
Robert J. Greaves ◽  
Wafik B. Beydoun
Keyword(s):  
Data Acquisition ◽  
Field Data ◽  
Large Scale ◽  
Reservoir Characterization ◽  
Synthetic Data ◽  
Velocity Model ◽  
Oil Field ◽  
Diffraction Tomography ◽  
Median Filters ◽  
Zero Offset

A crosswell seismic experiment at the San Emidio oil field in Bakersfield, California, is carried out to evaluate crosswell reflection/diffraction tomography and image the interwell region to locate a possible pinchout zone. In this experiment, the two wells used are 2500 ft (762 m) apart, and the zone to be imaged is 11 000 ft (3350 m) to 13 000 ft (3960 m) deep. With the considered distances, this experiment forms the first large scale reservoir characterization application of crosswell reflection/diffraction tomography. A subset of the intended data, formed of two common receiver gathers and one common shot gather, was collected at the San Emidio oil field. The crosswell data display a wide variety of wave modes including tube waves, singly and multiply reflected/diffracted waves, and refracted waves. The data are processed using frequency filters, median filters, and spatial muting filters to enhance the reflected/diffracted energy. A 2-D layered velocity model with gradients is built using zero‐offset VSPs and full‐waveform acoustic logs from the two wells. This model is used to generate synthetic finite‐difference data for the field data acquisition geometry. The synthetic data are processed and imaged using the elastic ray‐Born 𝓁2-migration/inversion (ERBMI) method. A smooth 2-D velocity model incorporating only gradients and a few layers is used as a background model for the imaging. Considering the limited data acquisition geometry, synthetic data images compare favorably with the initial velocity model. With the encouraging results obtained from synthetic data, the ERBMI method, with the smooth background velocity model is used next to image the processed field data. Images obtained from the crosswell data show a good match with the reflected field in the zero‐offset VSPs and with migrated surface seismic data. From the interpretation of these images, the potential of this crosswell seismic method for answering questions regarding reservoir continuity and existence of pinchout zones can be seen.

A new petrophysical joint inversion workflow: Advancing on reservoir’s characterization challenges

2018 ◽  
Vol 6 (3) ◽  
pp. SG33-SG39 ◽  
Author(s):  
Fabio Miotti ◽  
Andrea Zerilli ◽  
Paulo T. L. Menezes ◽  
João L. S. Crepaldi ◽  
Adriano R. Viana
Keyword(s):  
Reservoir Characterization ◽  
Joint Inversion ◽  
Oil Field ◽  
Well Log ◽  
Complementary Information ◽  
Reservoir Properties ◽  
Reservoir Rocks ◽  
Controlled Source ◽  
Fluid Properties

Reservoir characterization objectives are to understand the reservoir rocks and fluids through accurate measurements to help asset teams develop optimal production decisions. Within this framework, we develop a new workflow to perform petrophysical joint inversion (PJI) of seismic and controlled-source electromagnetic (CSEM) data to resolve for reservoirs properties. Our workflow uses the complementary information contained in seismic, CSEM, and well-log data to improve the reservoir’s description drastically. The advent of CSEM, measuring resistivity, brought the possibility of integrating multiphysics data within the characterization workflow, and it has the potential to significantly enhance the accuracy at which reservoir properties and saturation, in particular, can be determined. We determine the power of PJI in the retrieval of reservoir parameters through a case study, based on a deepwater oil field offshore Brazil in the Sergipe-Alagoas Basin, to augment the certainty with which reservoir lithology and fluid properties are constrained.

Dual-well 3D vertical seismic profile enabled by distributed acoustic sensing in deepwater Gulf of Mexico

2015 ◽  
Vol 3 (3) ◽  
pp. SW11-SW25 ◽  
Author(s):  
Han Wu ◽  
Wai-Fan Wong ◽  
Zhaohui Yang ◽  
Peter B. Wills ◽  
Jorge L. Lopez ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Velocity Model ◽  
Seismic Profile ◽  
Seismic Survey ◽  
Vertical Seismic Profile ◽  
Acoustic Sensing ◽  
Velocity Models ◽  
Vsp Data ◽  
Distributed Acoustic Sensing

We have acquired and processed 3D vertical seismic profile (VSP) data recorded simultaneously in two wells using distributed acoustic sensing (DAS) during the acquisition of the 2012 Mars 4D ocean-bottom seismic survey in the deepwater Gulf of Mexico. The objectives of the project were to assess the quality of DAS data recorded in fiber-optic cables from the surface to the total depth, to demonstrate the efficacy of the DAS VSP technology in a deepwater environment, to derisk the use of the technology for future water injection or production monitoring without intervention, and to exploit the velocity information that 3D VSP data provide for evaluating and updating the velocity model. We evaluated the advantages of DAS VSP to reduce costs and intrusiveness, and we determined that high-quality images can be obtained from relatively noisy raw 3D DAS VSP data, as evidenced by the well 1 image, probably the best 3D VSP image we have ever seen. Our results also revealed that the direct arrival traveltimes can be used to assess the quality of an existing velocity model and to invert for an improved velocity model. We identified issues with the DAS acquisition and the processing steps to mitigate them and to handle problems specific to DAS VSP data. We described the steps for conditioning the data before migration, reverse time migration, and postmigration processing to reduce noise artifacts. We outlined a novel first-break picking procedure that works even in the absence of a strong first arrival and a velocity diagnosis method to assess and validate velocity models and velocity updates. Finally, we determined potential applications to 4D monitoring of fluid movement around producer or injector wells, identification of active salt movements, and more accurate imaging and monitoring of complex structures around the wells.

scholarly journals 2D High-Resolution Crosswell Seismic Traveltime Tomography

2020 ◽  
Vol 25 (1) ◽  
pp. 47-53
Author(s):  
Chuan Li ◽  
JianXin Liu ◽  
Jianping Liao ◽  
Andrew Hursthouse
Keyword(s):  
High Resolution ◽  
Eastern China ◽  
Velocity Model ◽  
Well Logging ◽  
Oil Field ◽  
Traveltime Tomography ◽  
Reference Velocity ◽  
Velocity Information ◽  
High Resolution Reconstruction

This paper presents a method for combining the hybrid eikonal solver and the prior velocity information to obtain high-resolution crosswell imaging. The hybrid eikonal solver in this technique can ensure rapid and reliable forward modeling of traveltime field in an unsmoothed velocity model. We also utilize the sonic well logging curve to properly develop an initial reference velocity model, and use the sonic well logging data as the prior information for the inversion part, which can restrict the problem of non-uniqueness. The results of the numerical experiment of traveltime in multi-layer media showed that the hybrid eikonal solver was more accurate than the finite difference method. The case study of an oil field in eastern China demonstrated that our method can derive a high-resolution reconstruction of the subsurface structure by inverting the primary traveltime datasets. These results suggest that even though the eikonal equation is a high frequency approximation to the wavefield, the hybrid eikonal solver can provide an accurate traveltime field in the forward modelling step of seismic crosswell tomography, which is critical to ensure high-resolution invert imaging in a highly heterogeneous environment.

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