The Laggan and Tormore fields, Blocks 206/1 and 205/5, UK Atlantic Margin

2020 ◽  
Vol 52 (1) ◽  
pp. 967-979 ◽  
Author(s):  
J. Clark ◽  
P. Matthews ◽  
C. Parry ◽  
M. Rowlands ◽  
A. Tessier
Keyword(s):  
Reservoir Characterization ◽  
Reservoir Properties ◽  
Field Development ◽  
Shetland Islands ◽  
Development Scheme ◽  
Seismic Reservoir ◽  
Oil Rim ◽  
The Uk ◽  
Atlantic Margin ◽  
Area Development

AbstractThe Laggan and Tormore fields are found within the Flett sub-basin of the Faroe–Shetland Basin. Situated 120 km west of the Shetland Islands in 600 m water depth, they are part of the deepest subsea development in the UK to date with a 143 km subsea tie-back to onshore facilities.The reservoirs are found within the T35 biostratigraphic sequence of the Paleocene Vaila Formation and comprise sand-rich turbiditic channelized lobes with good reservoir properties, separated by metric to decimetric shale packages. Laggan is a gas-condensate field, whereas Tormore fluid is a richer gas with a saturated oil rim. Seismic reservoir characterization is a key to the field development where differentiation of fluid type proved challenging. Both fields came on stream in 2016 as part of the Greater Laggan area development scheme.

The Schiehallion and Loyal fields, Blocks 204/20, 204/25a, 204/25b, 205/16 and 205/21b, UK Atlantic Margin

2020 ◽  
Vol 52 (1) ◽  
pp. 990-1002 ◽  
Author(s):  
M. V. Ward ◽  
T. Primmer ◽  
E. Laws ◽  
K. Macgregor ◽  
T. Harpley ◽  
...  
Keyword(s):  
Deep Water ◽  
Seismic Survey ◽  
The West ◽  
Shetland Islands ◽  
Potential Sources ◽  
On Line ◽  
Mature Field ◽  
The Uk ◽  
4D Seismic ◽  
Atlantic Margin

AbstractThe Schiehallion subsea development comprises two fields, Schiehallion and Loyal, which are located approximately 200 km to the west of the Shetland Islands in the UK Continental Shelf. The Schiehallion and Loyal fields were discovered in late 1993 and 1994, respectively, with a combined oil-in-place of more than 2.3 Bbbl. The fields are developed under waterflood and were on production from 1998 to 2013. After an extended shut-in, the fields were brought back on line in 2017, through new floating production facilities.Most of the production to date has been from the Paleocene Vaila Formation deep-water turbidite, in the T31 and T34 reservoir intervals. The ongoing Quad 204 redevelopment drilling programme commenced in April 2015, has drilled and completed 21 wells to date, and is expected to continue for several more years. The campaign includes new producer–injector pairs and stand-alone wells to support existing well stock, targeting stacked turbidite reservoir intervals, including the youngest T35–T34 interval, the main T31 interval and the previously under-developed T28–T25 fairway.In addition to an active drilling programme, a 4D seismic survey was acquired and processed in 2018, and its interpretation is key to unlocking further potential sources of value in this mature field.

Quantitative seismic reservoir modeling — Model-based probabilistic inversion for optimal field development

2019 ◽  
Vol 38 (10) ◽  
pp. 786-790
Author(s):  
Yong Keun Hwang ◽  
Helena Zirczy ◽  
Sudhish Bakku
Keyword(s):  
Reservoir Modeling ◽  
Annual Business ◽  
Reservoir Properties ◽  
Full Field ◽  
Field Development ◽  
Model Based ◽  
Avo Inversion ◽  
Reservoir Models ◽  
Seismic Reservoir ◽  
Probabilistic Inversion

Full-field reservoir models provide key input to annual business plans and reserve booking. They support the long-term field development plan by enabling well target optimization, identification of infill opportunities, water-flood management, and well-surveillance and intervention strategies. It is crucial to constrain the model with all available static and dynamic data to improve its predictive power for confident decision making. Across Shell's global deepwater portfolio, a model-based probabilistic seismic amplitude-variation-with-offset (AVO) inversion methodology is used to constrain reservoir properties as part of a comprehensive quantitative seismic reservoir modeling workflow. Promise, a proprietary probabilistic inversion tool, estimates values of reservoir properties and quantifies their uncertainties through repeated forward modeling and automated quality checking of synthetic against recorded seismic data. During workflow execution, available geologic, petrophysical, and geophysical data are incorporated. As a consequence, the reservoir models are consistent with all relevant subsurface data following their update through inversion. Model-based inversion establishes a direct link between static model properties and elastic impedances. Probabilistic inversion output is an ensemble of posterior static models. The inversion process automatically sorts through the ensemble. It can directly provide low, mid, and high cases of the inverted models that are ready to be used in hydrocarbon volume estimation and multiscenario dynamic modeling for history matching and production forecasting. For successful and efficient delivery of full-field reservoir models with uncertainty assessment using model-based probabilistic AVO inversion, early integration of interdisciplinary subsurface data and cross-business collaboration are key.

The Clair Field, Blocks 206/7a, 206/8, 206/9a, 206/12a and 206/13a, UK Atlantic Margin

2020 ◽  
Vol 52 (1) ◽  
pp. 931-951 ◽  
Author(s):  
A. G. Robertson ◽  
M. Ball ◽  
J. Costaschuk ◽  
J. Davidson ◽  
N. Guliyev ◽  
...  
Keyword(s):  
Continental Shelf ◽  
Phase 1 ◽  
Hydrocarbon Accumulation ◽  
Well Test ◽  
Shallow Marine ◽  
Shetland Islands ◽  
Stock Tank ◽  
The Uk ◽  
Term Performance ◽  
Atlantic Margin

AbstractThe Clair Field is a giant oilfield containing in the region of 6–7 Bbbl of stock tank oil initially in place, located approximately 75 km west of the Shetland Islands. As such, it represents the single biggest hydrocarbon accumulation on the UK Continental Shelf. Clair was discovered in 1977, but first production did not occur from Phase 1 until 2005, after a lengthy appraisal period. The major appraisal milestone occurred in 1991 after well 206/8-8 proved up fractured clastic red beds of the Devonian Lower Clair Group. This was followed up with an extended well test on 206/8-10Z, which demonstrated the longer-term performance of the reservoir. Further appraisal on Clair Ridge led to the sanction of the Clair Ridge, which came on stream in November 2018. Following the Greater Clair appraisal programme in 2013–15, development options are currently being worked for Clair South, which will develop the Lower Clair Group reservoirs together with overlying shallow-marine reservoirs of the Cretaceous and Jurassic.

Predictive Seismic Reservoir Modelling in the UK Manx Basin

1992 ◽  
Vol 10 (4-5) ◽  
pp. 321-334 ◽  
Author(s):  
C.P. Ross ◽  
S.D. Flack
Keyword(s):  
Seismic Data ◽  
Seismic Reflection ◽  
Pore Fluid ◽  
Early Triassic ◽  
Reservoir Properties ◽  
Seismic Reflection Data ◽  
Reservoir Modelling ◽  
Reflection Data ◽  
Seismic Reservoir ◽  
The Uk

Within the Manx Basin, the primary reservoir is the early Triassic Helsby sandstone. The Helsby occurs in various facies, each with its own characteristic reservoir properties. Although seismic reflection data is insensitive to changes in the pore fluid fill, geophysical modelling has shown that it should be possible to map porosity variations on conventional seismic data.

scholarly journals Application of Artificial Neural Network in seismic reservoir characterization: a case study from Offshore Nile Delta

2021 ◽  
Author(s):  
Adel Othman ◽  
Mohamed Fathy ◽  
Islam A. Mohamed
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Nile Delta ◽  
Reservoir Properties ◽  
Seismic Amplitude ◽  
Inversion Methods ◽  
Amplitude Data ◽  
Seismic Reservoir

AbstractThe Prediction of the reservoir characteristics from seismic amplitude data is a main challenge. Especially in the Nile Delta Basin, where the subsurface geology is complex and the reservoirs are highly heterogeneous. Modern seismic reservoir characterization methodologies are spanning around attributes analysis, deterministic and stochastic inversion methods, Amplitude Variation with Offset (AVO) interpretations, and stack rotations. These methodologies proved good outcomes in detecting the gas sand reservoirs and quantifying the reservoir properties. However, when the pre-stack seismic data is not available, most of the AVO-related inversion methods cannot be implemented. Moreover, there is no direct link between the seismic amplitude data and most of the reservoir properties, such as hydrocarbon saturation, many assumptions are imbedded and the results are questionable. Application of Artificial Neural Network (ANN) algorithms to predict the reservoir characteristics is a new emerging trend. The main advantage of the ANN algorithm over the other seismic reservoir characterization methodologies is the ability to build nonlinear relationships between the petrophysical logs and seismic data. Hence, it can be used to predict various reservoir properties in a 3D space with a reasonable amount of accuracy. We implemented the ANN method on the Sequoia gas field, Offshore Nile Delta, to predict the reservoir petrophysical properties from the seismic amplitude data. The chosen algorithm was the Probabilistic Neural Network (PNN). One well was kept apart from the analysis and used later as blind quality control to test the results.

Synergistic integration of seismic and geologic data for modeling petrophysical properties

2020 ◽  
Vol 39 (3) ◽  
pp. 164-169
Author(s):  
Yuan Zee Ma ◽  
David Phillips ◽  
Ernest Gomez
Keyword(s):  
Neural Networks ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Integrated Approach ◽  
Reservoir Modeling ◽  
Petrophysical Properties ◽  
Reservoir Properties ◽  
Field Development ◽  
Realistic Description ◽  
Geologic Data

Reservoir characterization and modeling have become increasingly important for optimizing field development. Optimal valuation and exploitation of a field requires a realistic description of the reservoir, which, in turn, requires integrated reservoir characterization and modeling. An integrated approach for reservoir modeling bridges the traditional disciplinary divides and tears down interdisciplinary barriers, leading to better handling of uncertainties and improvement of the reservoir model for field development. This article presents the integration of seismic data using neural networks and the incorporation of a depositional model and seismic data in constructing reservoir models of petrophysical properties. Some challenging issues, including low correlation due to Simpson's paradox and under- or overfitting of neural networks, are mitigated in geostatistical analysis and modeling of reservoir properties by integrating geologic information. This article emphasizes the integration of well logs, seismic prediction, and geologic data in the 3D reservoir-modeling workflow.

The Edradour Field, Block 206/4a, UK Atlantic Margin

2020 ◽  
Vol 52 (1) ◽  
pp. 952-957 ◽  
Author(s):  
J. Clark ◽  
D. Mazzuchelli ◽  
M. Rowlands ◽  
N. Jebara ◽  
C. Parry
Keyword(s):  
Water Depth ◽  
Gas Condensate ◽  
Shetland Islands ◽  
Joint Development ◽  
Development Scheme ◽  
Single Well ◽  
Well Development ◽  
Atlantic Margin

AbstractThe Edradour Field, located in Licence P1453 on Block 206/4a of the Faroe–Shetland Basin, was put on production in August 2017. It lies c. 50 km NW of the Shetland Islands in a water depth of c. 300 m, and consists of one subsea well that produces gas condensate from the Albian Black Sail Member of the Commodore Formation. It is part of a joint development scheme along with the Glenlivet Field that sees the commingled multiphase production transported to the Shetland Gas Plant via tieback to the pre-existing Laggan–Tormore flowlines. The Edradour single well development has reserves of 21 MMboe from a gas initially-in-place of 142 bcf. It is operated by Total E&P UK Ltd under the P1453 licence with Ineos E&P (UK) Ltd and SSE E&P UK Ltd as partners.

scholarly journals Artificial Intelligence Aided Geologic Facies Classification in Complex Carbonate Reservoirs

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
Alberto Marsala ◽  
Yanhui Zhang ◽  
Ibrahim Hoteit
Keyword(s):  
Artificial Intelligence ◽  
Reservoir Characterization ◽  
High Efficiency ◽  
Support Vector ◽  
Well Log ◽  
Reservoir Properties ◽  
Model Learning ◽  
Field Development ◽  
Facies Classification ◽  
Classification Framework

Abstract Facies classification for complex reservoirs is an important step in characterizing reservoir heterogeneity and determining reservoir properties and fluid flow patterns. Predicting rock facies automatically and reliably from well log and associated reservoir measurements is therefore essential to obtain accurate reservoir characterization for field development in a timely manner. In this study, we present an artificial intelligence (AI) aided rock facies classification framework for complex reservoirs based on well log measurements. We generalize the AI-aided classification workflow into five major steps including data collection, preprocessing, feature engineering, model learning cycle, and model prediction. In particular, we automate the process of facies classification focusing on the use of a deep learning technique, convolutional neural network, which has shown outstanding performance in many scientific applications involving pattern recognition and classification. For performance analysis, we also compare the developed model with a support vector machine approach. We examine the AI-aided workflow on a large open dataset acquired from a real complex reservoir in Alberta. The dataset contains a collection of well-log measurements over a couple of thousands of wells. The experimental results demonstrate the high efficiency and scalability of the developed framework for automatic facies classification with reasonable accuracy. This is particularly useful when quick facies prediction is necessary to support real-time decision making. The AI-aided framework is easily implementable and expandable to other reservoir applications.

scholarly journals Hydrocarbon Prospectivity in the undrilled area of AIMA Field in the Niger Delta Basin, Nigeria.

2021 ◽  
Vol 6 (1) ◽  
pp. 34-41
Author(s):  
Alexander Ogbamikhumi ◽  
John Elvis Ighodalo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Niger Delta ◽  
Water Saturation ◽  
Poisson Ratio ◽  
Rock Physics ◽  
Reservoir Properties ◽  
Field Development ◽  
Seismic Reservoir ◽  
Niger Delta Basin

Field development is a very costly endeavor that requires drilling several wells in an attempt to understanding potential prospects. To help reduce the associated cost, this study integrates well and seismic based rock physics analysis with artificial neural network to evaluation identified prospects in the field.  Results of structural and amplitude maps of three major reservoir levels revealed structural highs typical of roll over anticlines with amplitude expression that conforms to structure at the exploited zone where production is currently ongoing. Across the bounding fault to the prospective zones, only the D_2 reservoir possessed the desired amplitude expression, typical of hydrocarbon presence. To validate the observed amplitude expression at the prospective zone, well and seismic based rock physics analyses were performed. Results from the analysis presented Poisson ratio, Lambda-Rho and Lambda/Mu-Rho ratio as good fluid indicator while Mu-Rho was the preferred lithology indicator.  These rock physics attributes were employed to validate the observed prospective direct hydrocarbon indicator  expressions on seismic. Reservoir properties maps generated for porosity and water saturation prediction using Probability Neural Network gave values of 20-30% and 25-35% for water saturation and porosity respectively, indicating  the presence of good quality hydrocarbon bearing reservoir at the prospective zone.

The Goldeneye Field, Blocks 14/29a and 20/4b, UK North Sea

2020 ◽  
Vol 52 (1) ◽  
pp. 755-766 ◽  
Author(s):  
Nicola Stewart ◽  
John D. Marshall
Keyword(s):  
Carbon Capture ◽  
Carbon Capture And Storage ◽  
Field Development ◽  
Water Breakthrough ◽  
Production Wells ◽  
Moray Firth ◽  
Remaining Capacity ◽  
Oil Rim ◽  
The Uk ◽  
And Storage

AbstractThe Goldeneye gas-condensate field lies in the Moray Firth Basin in the UK Continental Shelf (UKCS) approximately 100 km off the NE coast of Scotland. The field was discovered in 1996 as a normally pressured accumulation with estimated gas-initially-in-place (GIIP) of 810 bcf with a thin oil rim in the Lower Cretaceous Captain Sandstone Member in a three-way, dip-closed structure. Field development included five production wells, with first gas achieved in 2004. Goldeneye was steadily produced under moderate aquifer support until cessation of production (COP) in 2010 following water breakthrough at the wells. Over its lifetime Goldeneye has produced 568 bcf of gas and 23 MMbbl of condensate.Around the time of COP, the UK Carbon Capture and Storage Commercialisation Competition was announced, and Goldeneye was evaluated as a candidate. The removal of significant volumes of hydrocarbons through production left remaining capacity that could be refilled without reservoir pressure significantly exceeding virgin conditions. However, following withdrawal of funding from the UK Government in 2015, the project was put on hold. Since then additional subsurface work has been conducted to support the successful abandonment of the development wells, which had previously been suspended since 2010.

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