Borehole seismic surveys for fault delineation in the Dutch North Sea

Geophysics ◽  
1989 ◽  
Vol 54 (9) ◽  
pp. 1091-1100 ◽  
Author(s):  
N. J. van der Poel ◽  
B. R. Cassell
Keyword(s):  
North Sea ◽  
Seismic Data ◽  
Hybrid Approach ◽  
Gas Production ◽  
Reflection Point ◽  
Seismic Profiles ◽  
Point Mapping ◽  
Field Development ◽  
Precise Knowledge ◽  
Borehole Seismic

A significant share of gas production in the Dutch sector of the southern North Sea Basin comes from Permian Rotliegend fault blocks. Precise knowledge of the positions of these faults is necessary for efficient exploitation of the reservoir structures and for future field development strategies. Two areas are presented where the lateral resolution of the surface seismic data was not sufficient to determine positions of major fault block boundaries accurately. Walkaway borehole seismic profiles were shot over each of these areas with the objective of illuminating the fault boundaries to obtain an image with a higher resolution. The images were generated using borehole seismic reflection‐point mapping and migration techniques. Large‐aperture migrations tend to produce unacceptable migration smiles, while reflection‐point mapping is a model‐dependent process. A hybrid approach to these processes was necessary to avoid problems associated with the limited angular illumination permitted by the field acquisition geometries. Reliable images of the fault boundaries were obtained using migration apertures of less than ±5° relative to the structural dips in the background model and by matching that model with the surface seismic and borehole seismic data. The stability of the process and, therefore, the accuracy of the lateral positioning were verified by testing the migration process using a range of apertures.

The Brent Field, Block 211/29, UK North Sea

2003 ◽  
Vol 20 (1) ◽  
pp. 233-250 ◽  
Author(s):  
S. R. Taylor ◽  
J. Almond ◽  
S. Arnott ◽  
D. Kemshell ◽  
D. Taylor
Keyword(s):  
North Sea ◽  
Seismic Data ◽  
Water Injection ◽  
Development Project ◽  
Lower Jurassic ◽  
Gas Production ◽  
The North ◽  
The United Kingdom ◽  
Wet Gas ◽  
The North Sea

AbstractThe Brent Field was the first discovery in the northern part of the North Sea, and is one of the largest hydrocarbon accumulations in the United Kingdom licence area. There are two separate major accumulations: one in the Middle Jurassic (Brent Group reservoir) and one in the Lower Jurassic/Triassic (Statfjord Formation reservoir). The Brent Field lies entirely within UK licence Block 211/29 at latitude 61°N and longitude 2°E; the adjacent Brent South accumulation extends into Block 3/4A. The water depth is 460 ft. The Brent Field discovery well was drilled in 1971, and was followed by six further exploration and appraisal wells. Seismic data over the Brent Field has been acquired in four separate vintages. The latest acquisition in 1995 allowed detailed mapping of the complex eastern margin of the field for the first timeThe Brent Field is developed from four fixed platforms (Alpha, Bravo, Charlie, Delta) installed between 1975 and 1978. Production commenced in 1976 and, for the first 22 years of field life, the platforms provided production, water injection and gas injection facilities for both the Brent and Statfjord Formation reservoirs. The Brent South accumulation is produced via the Brent Alpha platform, through sub-sea tie-backs and extended reach wells. In 1992, the decision was taken to depressurize the Brent Field to recover an additional 1.5 TSCF of gas and 34 MMSTB of oil, extending the field's life by 5-10 years. In January 1998, water injection into the main field was stopped and depressurization of the field initiated. As of January 2000, a total of 220 platform wells and three sub-sea wells (173 producers, 50 water injectors) have been drilled in the Brent Field.The original oil/condensate-in-place is currently estimated at 3.8 MMMSTB, and the estimated original wet gas-in-place is 7.5 TSCF. Total ultimate recovery for all reservoirs is expected to be 1988 MMSTB oil and condensate and 6000 BSCF gas. Cumulative oil and net gas production, as of 1st January 2000, was 1875 MMSTB oil and 4196 BSCF gasThis paper summarizes the current understanding of the field based on acquisition of new 3D seismic data, 130 new wells, detailed structural and sedimentological modelling, development of the complex crestal part of the field and finally, the initiation of an extensive brown field re-development project to depressurize the reservoir

Reconstruction of reflecting structures from vertical seismic profiles with a moving source

Geophysics ◽  
1986 ◽  
Vol 51 (10) ◽  
pp. 1923-1938 ◽  
Author(s):  
K. Köhler ◽  
M. Koenig
Keyword(s):  
Seismic Profile ◽  
Seismic Velocities ◽  
Reflection Point ◽  
Constant Depth ◽  
Seismic Profiles ◽  
Point Mapping ◽  
Resolution Power ◽  
Vertical Seismic Profiles ◽  
Kirchhoff Migration ◽  
Walkaway Vsp

When a vertical seismic profile (VSP) is recorded, the illuminated part of a reflector depends upon the shape and position of the reflector itself as well as on the seismic velocities and the positions of sources and receivers. A preferable arrangement for the investigation of structures of reflectors is to fix the receiver(s) at constant depth(s) in the well and move the source horizontally along a line at the Earth’s surface, usually called a “multioffset VSP” (MSP) or “walkaway VSP.” As a test of the resolution power of this survey geometry, synthetic records were generated from a subsurface model by inverse Kirchhoff migration. Three different methods were applied for the reconstruction. Wavefront construction leads to the correct shape of the reflectors, thus assuring the validity of the modeling method applied. Reflection‐point mapping delivered a near similarity to the model, but without focusing fault edges. Kirchhoff migration resulted in a detailed image of the reflectors with fault edges focused. Application of reflection‐point mapping and Kirchhoff migration to a real survey delivered results consistent with results from a survey at the Earth’s surface.

Combining Wireline and LWD Borehole Seismic Data for Drilling HPHT Well: A Novel Approach

2009 ◽  
Author(s):  
Teck Kean Lim ◽  
Aqil Ahmed ◽  
Muhammad Antonia Gibrata ◽  
Gunawan Taslim
Keyword(s):  
Seismic Data ◽  
Novel Approach ◽  
Borehole Seismic

Integrated Subsurface to Surface Modeling to Assess Reservoir Uncertainty Quantification to A Carbonate Reservoir

2020 ◽  
Author(s):  
A. Chaterine
Keyword(s):  
Uncertainty Quantification ◽  
Gas Production ◽  
Carbonate Reservoir ◽  
Production Volume ◽  
Production Forecast ◽  
Uncertainty Range ◽  
Field Development ◽  
Production Forecasting ◽  
Production Scheme ◽  
Rock Compressibility

This study accommodates subsurface uncertainties analysis and quantifies the effects on surface production volume to propose the optimal future field development. The problem of well productivity is sometimes only viewed from the surface components themselves, where in fact the subsurface component often has a significant effect on these production figures. In order to track the relationship between surface and subsurface, a model that integrates both must be created. The methods covered integrated asset modeling, probability forecasting, uncertainty quantification, sensitivity analysis, and optimization forecast. Subsurface uncertainties examined were : reservoir closure, regional segmentation, fluid contact, and SCAL properties. As the Integrated Asset Modeling is successfully conducted and a matched model is obtained for the gas-producing carbonate reservoir, highlights of the method are the following: 1) Up to ± 75% uncertainty range of reservoir parameters yields various production forecasting scenario using BHP control with the best case obtained is 335 BSCF of gas production and 254.4 MSTB of oil production, 2) SCAL properties and pseudo-faults are the most sensitive subsurface uncertainty that gives major impact to the production scheme, 3) EOS modeling and rock compressibility modeling must be evaluated seriously as those contribute significantly to condensate production and the field’s revenue, and 4) a proposed optimum production scenario for future development of the field with 151.6 BSCF gas and 414.4 MSTB oil that yields a total NPV of 218.7 MMUSD. The approach and methods implemented has been proven to result in more accurate production forecast and reduce the project cost as the effect of uncertainty reduction.

scholarly journals New Hybrid Approach for Developing Automated Machine Learning Workflows: A Real Case Application in Evaluation of Marcellus Shale Gas Production

Fuels ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 286-303
Author(s):  
Vuong Van Pham ◽  
Ebrahim Fathi ◽  
Fatemeh Belyadi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Shale Gas ◽  
Field Data ◽  
Marcellus Shale ◽  
Hybrid Approach ◽  
Gas Production ◽  
Bayesian Optimization ◽  
Real Field ◽  
Engineering Problems

The success of machine learning (ML) techniques implemented in different industries heavily rely on operator expertise and domain knowledge, which is used in manually choosing an algorithm and setting up the specific algorithm parameters for a problem. Due to the manual nature of model selection and parameter tuning, it is impossible to quantify or evaluate the quality of this manual process, which in turn limits the ability to perform comparison studies between different algorithms. In this study, we propose a new hybrid approach for developing machine learning workflows to help automated algorithm selection and hyperparameter optimization. The proposed approach provides a robust, reproducible, and unbiased workflow that can be quantified and validated using different scoring metrics. We have used the most common workflows implemented in the application of artificial intelligence (AI) and ML in engineering problems including grid/random search, Bayesian search and optimization, genetic programming, and compared that with our new hybrid approach that includes the integration of Tree-based Pipeline Optimization Tool (TPOT) and Bayesian optimization. The performance of each workflow is quantified using different scoring metrics such as Pearson correlation (i.e., R2 correlation) and Mean Square Error (i.e., MSE). For this purpose, actual field data obtained from 1567 gas wells in Marcellus Shale, with 121 features from reservoir, drilling, completion, stimulation, and operation is tested using different proposed workflows. A proposed new hybrid workflow is then used to evaluate the type well used for evaluation of Marcellus shale gas production. In conclusion, our automated hybrid approach showed significant improvement in comparison to other proposed workflows using both scoring matrices. The new hybrid approach provides a practical tool that supports the automated model and hyperparameter selection, which is tested using real field data that can be implemented in solving different engineering problems using artificial intelligence and machine learning. The new hybrid model is tested in a real field and compared with conventional type wells developed by field engineers. It is found that the type well of the field is very close to P50 predictions of the field, which shows great success in the completion design of the field performed by field engineers. It also shows that the field average production could have been improved by 8% if shorter cluster spacing and higher proppant loading per cluster were used during the frac jobs.

Acquire Ocean Bottom Seismic Data and Time-Lapse Geochemistry Data Simultaneously to Identify Compartmentalization and Map Hydrocarbon Movement

2021 ◽  
Author(s):  
Rick Schrynemeeckers
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Time Lapse ◽  
Field Trials ◽  
Detection Methods ◽  
Ocean Bottom ◽  
Field Development ◽  
Dense Grid ◽  
Hydrocarbon Charge ◽  
Sweet Spots

Abstract Current offshore hydrocarbon detection methods employ vessels to collect cores along transects over structures defined by seismic imaging which are then analyzed by standard geochemical methods. Due to the cost of core collection, the sample density over these structures is often insufficient to map hydrocarbon accumulation boundaries. Traditional offshore geochemical methods cannot define reservoir sweet spots (i.e. areas of enhanced porosity, pressure, or net pay thickness) or measure light oil or gas condensate in the C7 – C15 carbon range. Thus, conventional geochemical methods are limited in their ability to help optimize offshore field development production. The capability to attach ultrasensitive geochemical modules to Ocean Bottom Seismic (OBS) nodes provides a new capability to the industry which allows these modules to be deployed in very dense grid patterns that provide extensive coverage both on structure and off structure. Thus, both high resolution seismic data and high-resolution hydrocarbon data can be captured simultaneously. Field trials were performed in offshore Ghana. The trial was not intended to duplicate normal field operations, but rather provide a pilot study to assess the viability of passive hydrocarbon modules to function properly in real world conditions in deep waters at elevated pressures. Water depth for the pilot survey ranged from 1500 – 1700 meters. Positive thermogenic signatures were detected in the Gabon samples. A baseline (i.e. non-thermogenic) signature was also detected. The results indicated the positive signatures were thermogenic and could easily be differentiated from baseline or non-thermogenic signatures. The ability to deploy geochemical modules with OBS nodes for reoccurring surveys in repetitive locations provides the ability to map the movement of hydrocarbons over time as well as discern depletion affects (i.e. time lapse geochemistry). The combined technologies will also be able to: Identify compartmentalization, maximize production and profitability by mapping reservoir sweet spots (i.e. areas of higher porosity, pressure, & hydrocarbon richness), rank prospects, reduce risk by identifying poor prospectivity areas, accurately map hydrocarbon charge in pre-salt sequences, augment seismic data in highly thrusted and faulted areas.

Potentially Large Subsurface Gas Hydrate Bodies in the Mexican Ridges, Southwestern Gulf of Mexico

2021 ◽  
pp. 1-29
Author(s):  
Papia Nandi ◽  
Patrick Fulton ◽  
James Dale
Keyword(s):  
Gulf Of Mexico ◽  
Gas Hydrate ◽  
Seismic Data ◽  
Poor Quality ◽  
Reflection Point ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
High Impedance ◽  
Acoustic Velocities ◽  
Seismic Images

As rising ocean temperatures can destabilize gas hydrate, identifying and characterizing large shallow hydrate bodies is increasingly important in order to understand their hazard potential. In the southwestern Gulf of Mexico, reanalysis of 3D seismic reflection data reveals evidence for the presence of six potentially large gas hydrate bodies located at shallow depths below the seafloor. We originally interpreted these bodies as salt, as they share common visual characteristics on seismic data with shallow allochthonous salt bodies, including high-impedance boundaries and homogenous interiors with very little acoustic reflectivity. However, when seismic images are constructed using acoustic velocities associated with salt, the resulting images were of poor quality containing excessive moveout in common reflection point (CRP) offset image gathers. Further investigation reveals that using lower-valued acoustic velocities results in higher quality images with little or no moveout. We believe that these lower acoustic values are representative of gas hydrate and not of salt. Directly underneath these bodies lies a zone of poor reflectivity, which is both typical and expected under hydrate. Observations of gas in a nearby well, other indicators of hydrate in the vicinity, and regional geologic context, all support the interpretation that these large bodies are composed of hydrate. The total equivalent volume of gas within these bodies is estimated to potentially be as large as 1.5 gigatons or 10.5 TCF, considering uncertainty for estimates of porosity and saturation, comparable to the entire proven natural gas reserves of Trinidad and Tobago in 2019.

Artificial Intelligence Assisted Well Portfolio Optimization - An Automated Reservoir Management Advisory System to Maximize the Asset Value - Case Study from ADNOC Onshore

2021 ◽  
Author(s):  
Subba Ramarao Rachapudi Venkata ◽  
Nagaraju Reddicharla ◽  
Shamma Saeed Alshehhi ◽  
Indra Utama ◽  
Saber Mubarak Al Nuimi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Approach ◽  
Simulation Models ◽  
Production Optimization ◽  
Added Value ◽  
Risk Scores ◽  
Well Performance ◽  
Field Development ◽  
Advisory System ◽  
Gas Lift

Abstract Matured hydrocarbon fields are continuously deteriorating and selection of well interventions turn into critical task with an objective of achieving higher business value. Time consuming simulation models and classical decision-making approach making it difficult to rapidly identify the best underperforming, potential rig and rig-less candidates. Therefore, the objective of this paper is to demonstrate the automated solution with data driven machine learning (ML) & AI assisted workflows to prioritize the intervention opportunities that can deliver higher sustainable oil rate and profitability. The solution consists of establishing a customized database using inputs from various sources including production & completion data, flat files and simulation models. Automation of Data gathering along with technical and economical calculations were implemented to overcome the repetitive and less added value tasks. Second layer of solution includes configuration of tailor-made workflows to conduct the analysis of well performance, logs, output from simulation models (static reservoir model, well models) along with historical events. Further these workflows were combination of current best practices of an integrated assessment of subsurface opportunities through analytical computations along with machine learning driven techniques for ranking the well intervention opportunities with consideration of complexity in implementation. The automated process outcome is a comprehensive list of future well intervention candidates like well conversion to gas lift, water shutoff, stimulation and nitrogen kick-off opportunities. The opportunity ranking is completed with AI assisted supported scoring system that takes input from technical, financial and implementation risk scores. In addition, intuitive dashboards are built and tailored with the involvement of management and engineering departments to track the opportunity maturation process. The advisory system has been implemented and tested in a giant mature field with over 300 wells. The solution identified more techno-economical feasible opportunities within hours instead of weeks or months with reduced risk of failure resulting into an improved economic success rate. The first set of opportunities under implementation and expected a gain of 2.5MM$ with in first one year and expected to have reoccurring gains in subsequent years. The ranked opportunities are incorporated into the business plan, RMP plans and drilling & workover schedule in accordance to field development targets. This advisory system helps in maximizing the profitability and minimizing CAPEX and OPEX. This further maximizes utilization of production optimization models by 30%. Currently the system was implemented in one of ADNOC Onshore field and expected to be scaled to other fields based on consistent value creation. A hybrid approach of physics and machine learning based solution led to the development of automated workflows to identify and rank the inactive strings, well conversion to gas lift candidates & underperforming candidates resulting into successful cost optimization and production gain.

The Clipper Field, Blocks 48/19a, 48/19c, UK North Sea

2003 ◽  
Vol 20 (1) ◽  
pp. 691-698
Author(s):  
M. J. Sarginson
Keyword(s):  
North Sea ◽  
Upper Permian ◽  
Lower Permian ◽  
Reservoir Properties ◽  
Gas Field ◽  
Field Development ◽  
Matrix Permeability ◽  
Recoverable Reserves ◽  
Reservoir Permeability ◽  
Sandstone Formation

AbstractThe Clipper Gas Field is a moderate-sized faulted anticlinal trap located in Blocks 48/19a, 48/19c and 48/20a within the Sole Pit area of the southern North Sea Gas Basin. The reservoir is formed by the Lower Permian Leman Sandstone Formation, lying between truncated Westphalian Coal Measures and the Upper Permian evaporitic Zechstein Group which form source and seal respectively. Reservoir permeability is very low, mainly as a result of compaction and diagenesis which accompanied deep burial of the Sole Pit Trough, a sub basin within the main gas basin. The Leman Sandstone Formation is on average about 715 ft thick, laterally heterogeneous and zoned vertically with the best reservoir properties located in the middle of the formation. Porosity is fair with a field average of 11.1%. Matrix permeability, however, is less than one millidarcy on average. Well productivity depends on intersecting open natural fractures or permeable streaks within aeolian dune slipface sandstones. Field development started in 1988. 24 development wells have been drilled to date. Expected recoverable reserves are 753 BCF.

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