The characterization of the clastic Zubair reservoir is challenging because of the high lamination and the oil properties change making the conventional saturation technique uncertain. A new workflow has been recently established in the newly appraised wells which has involved advanced petrophysical measurements along with the fluid sampling. The new technique has led to identify new HC layers that were overlooked by the previous techniques, thus adding more reserves to the KOC asset.
Because of the high lamination of clastic Zubair formation and the change of the oil properties, the dielectric dispersion measurement was integrated along with the diffusion-based NMR to identify new oil zones that has been initially masked by the resistivity-based approach. The new approach has also provided details on the oil movability and the characterization of its property. As the newly identified layers were identified for the 1st time across the field, the fluid sampling was conducted to confirm the new findings.
The advent of a new logging technology from a multi-frequency dielectric technique deployed over the formation has independently pinned down the HC pays over the Zubair interval, including a new zone below the water column. The zone was initially identified as heavy Tar zone. The advanced diffusion-based NMR was thus conducted and integrated with Dielectrics which has demonstrated the movability of HC using the diffusion-based NMR approach over the newly identified zone. A fluid sampling was later performed which has confirmed the new finding. The new identified zone was initially overlooked by the previous interpretation and extensive modeling over the entire field. The seal mechanism was also explained by taking advantage of the high-resolution dielectric dispersion measurement (mainly the low frequency), which has been also supported by the Images interpretation. This new approach has added an incremental oil storage over the field.
Su, Qin (Research Institute of Petroleum Exploration & Development-Northwest, NWGI, PetroChina) | Zeng, Huahui (Research Institute of Petroleum Exploration & Development-Northwest, NWGI, PetroChina) | Zhang, Xiaomei (Research Institute of Petroleum Exploration & Development-Northwest, NWGI, PetroChina) | Lv, Lei (Research Institute of Petroleum Exploration & Development-Northwest, NWGI, PetroChina) | Qie, Shuhai (Research Institute of Petroleum Exploration & Development-Northwest, NWGI, PetroChina) | Meng, Huijie (Research Institute of Petroleum Exploration & Development-Northwest, NWGI, PetroChina)
With the continuous development of oil and gas exploration technology, the remaining exploration targets in the middle and shallow areas of the land are becoming less and less, and the deep complex targets have become an important replacement area for oil and gas growth. In order to enhance the deep tight gas exploration potential of the Songliao Basin in China, structural interpretation and reservoir prediction of deep volcanic rocks and glutenite lithologic gas reservoirs are carried out, while the basic requirements for seismic data acquisition in complex reservoir exploration in the middle-deep layers are: higher sampling density, even distribution of space, appropriate offset. In the face of particularly complex reservoirs, it is necessary to fully strengthen the acquisition parameters, ensure the reservoir prediction needs, and avoid the waste caused by the inability to solve the geological problems. However, due to the weak signal and strong interference, the conventional narrowazimuth three-dimensional observation system in the Songliao Basin is affected by factors such as low folds, large grid bin, and low reception of complex structural information, which affects the middledeep layers. The imaging effect has restricted the development of tight gas exploration in the middledeep layers. Therefore, the broadband, wide-azimuth and high-density (BWH) 3D seismic exploration technology has been developed. BWH refers to a wider excitation and reception frequency band, a wider reception orientation, and a higher sampling density. Generally, broadband acquisition requires signals with an octave of more than 5 times; wide-azimuth observation systems should have an aspect ratio greater than 0.5, where an aspect ratio greater than 0.85 is called an omnidirectional observation system; when using an explosive source, sampling densities greater than 500,000 channels/km
Equilibrium Pc-RI measurements on low permeability core plugs present the SCAL laboratory with some difficult challenges regarding the duration of measurements and the attainment of truly equilibrated resistance readings. A new empirical method is described that allows estimation of fully equilibrated resistance by application of a simple transient data linearizing transform and plot slope analysis. A small set of plugs from a conventional tight gas field in the Sultanate of Oman is used to demonstrate the method. The method may also be used by the lab to monitor and shorten the Pc-RI measurement duration without compromising the interpretation of saturation exponents or capillary curves. Transform plot transients and macro capillary number are examined to estimate a boundary where the plugs transition from shock front rapid desaturation to slow percolation desaturation behavior.
Pramudyo, Yuni (ADNOC Onshore) | Al Hosani, Mariam (ADNOC Onshore) | Al Awadhi, Fatima (ADNOC Onshore) | Masoud, Rashad (ADNOC Onshore) | Al Besr, Huda (ADNOC Onshore) | Nachiappan, Ramanathan (ADNOC Onshore) | Al Hosani, Khaled (ADNOC Onshore) | Al Bairaq, Ahmed (ADNOC Onshore) | Al Ameri, Ammar (ADNOC Onshore) | Bertouche, Meriem (Badley Ashton) | Foote, Alexander (Badley Ashton) | Michie, Emma (Badley Geoscience) | Yielding, Graham (Badley Geoscience)
Throughout the UAE and the wider region, several broadly E-W orientated structural lineaments are observed on seismic within the Cretaceous successions and are described as strike-slip faults. However, in the studied field, these features have not been readily observed in well data. Instead, networks of fractures and deformation features are present in core and borehole images. A study was carried out in an attempt to calibrate well and seismic data and to understand the relationship between the seismically-resolved faults and the fractures observed on core. This study focuses on a dataset from the north-east part of the field, which includes BHI images, cores, full 3D CT scans and conventional logs in four penetrations, three of which are horizontal, drilled through the faults; as well as 3D seismic data and relevant derived horizons and fault polygon interpretations.
The available data have been investigated in detail, with all structural features in core, circumferential CT scans and BHI images systematically classified using simple and reproducible descriptive schemes. All the structural features have been orientated using directional data from BHI. The understanding of the character and fill of the fractures observed in core has also been incorporated. A further calibration with seismic and integration of results with information from previous studies allowed a full description of the fracture networks, of their densities within and outside the potential fault corridors of the studied field, as well as an assessment of their potential for reactivation and their possible impact on localised formation compaction.
On the BHI images, several sub-vertical fractures have been identified, consisting mainly of mixed resistivity and resistive fractures, striking dominantly WNW-ESE. Particular zones along the wells have noticeably higher fracture densities, where features are organised in clusters; they are intercalated with zones where fractures are rarer. The clustering of fractures within fracture corridors are believed to be fault-related, subvertical and tabular fracture clusters that traverse an entire reservoir unit vertically and extend for several hundreds to thousands of feet laterally. These zones are believed to represent fracture corridors, which correlate with the structural lineaments observed on seismic.
The fracture corridor network in the study area shows a variable deformation signature at the different scales of observations, but consists mainly of sub-vertical (dominantly >60°) deformation bands (
Jain, Siddharth (Sharjah National Oil Corporation) | Al Hamadi, Masoud (Sharjah National Oil Corporation) | Stewart, Neil (Sharjah National Oil Corporation) | Khan, Sikandar (LMKR) | Malik, Arshad (LMKR)
Capitalizing on untapped potentials and quantifying risk is the key to success in a turbulent commodity industry. With an internal initiative to capture national knowledge and to safe guard sovereign data, Sharjah National Oil Corporation (SNOC) embarked on a multi-year journey to find, archive, digitize and integrate the entire E&P data in the Emirate dating from 1966 till date. The challenges and accomplishments of effective data management are demonstrated as a successful case study.
Securing investment in a country’s petroleum industry demands for government agencies to add value to its oil & gas assets by making them readily available, globally. To do so, one of the most important steps was to effectively catalogue and manage technical information from both past as well as present exploration and production initiatives. Scattered archives for multi-disciplinary data including well reports, logs, maps, seismic sections, tapes, G&G studies, cores, cuttings and digital databases was centralized in one repository for the Emirate. A unified data quality assessment, metadata capture & naming convention was established for all data types including a technology infrastructure to enable maximum utilization and integration of data into future projects. Ambitious to exploit its current technical assets by evaluating historic data to attain maximum benefit to SNOC, partners and global investors; SNOC adopted a cohesive and effective physical & digital asset management approach with a phased methodology in early 2016. This data management activity was classified extremely critical to the E&P current and upcoming projects by laying the foundation for drilling SNOC’s 1st Exploration well, a successful Farm-out bid round, quantifying workover opportunities, regional interpretation and performing field development simulations. Centralizing and integrating the data management function enabled the operator to identify data gaps and work on fulfilling them to successfully establish a regional technical data center. The paper discusses the challenges of gathering, extracting and incorporating legacy data; shedding light on the value it generated in paving the way for a digital transformation in SNOC’s E&P business.
Fluctuating technological, political and business influences add to the volatility and risk in the oil and gas business. The framework and success of future opportunities is reliant on the data used to quantify them. The method chosen for this journey was to modularize the data management project and structure best in class industry solutions for each aspect. The architecture for data management implemented is cost-effective; which is scalable and upgradable with minimum effort into a digital hub of information & knowledge management.
This paper presents the successful application of a new-generation slim pulsed neutron logging tool for identification of bypassed oil in the Nong Yao field. The field comprises of different small pools of oil developed with horizontal wells. The wells are drilled with long lateral sections to increase the drainage area in an attempt to increase sweep efficiencies. However, the sweep efficiencies remained uncertain given reservoir heterogeneity and the nature of water encroachment into the wells.
Reservoir saturation monitoring through tubing is usually required for an effective reservoir management program in such a mature field, and a cost-effective method for future opportunity identification. The traditional slim pulsed neutron logging (PNL) tools often provided inconclusive results especially when deployed in complex completion conditions. A new-generation slim pulsed neutron logging tool, which provides high-resolution spectroscopy with a much-improved accuracy and precision was investigated and introduced. This tool delivers self-compensated sigma and neutron porosity measurements in a wide range of conditions, including complex completions and with varying amount of gas in the wellbore or annulus.
This new PNL tool was run in the Nong Yao field in December 2017 with the objective to prove the remaining oil at the top of a reservoir. The objective was to acquire data in GSH (sigma, fast neutron cross section, Porosity) and IC (spectroscopy) modes in 8-1/2" hole with conventional completion (7" casing + 2-7/8" tubing). Despite challenging borehole fluid conditions, the data acquired confirmed remaining oil in the reservoir and a new well drilled in 2018 targeting this bypassed oil is currently producing with very good oil production.
This successful implementation of PNL in 2017 led to the adoption of the tool as a good alternative for confirming bypassed oil in the Nong Yao field. This strategy has been adopted for well target validation and horizontal well placement to support the 2019-2020 infill drilling campaigns.
In December 2018, this tool was run again in three selected candidate wells to prove the remaining bypassed oil and oil saturation away from currently producing wells. The results acquired in all three cases showed clear oil/water contact movement and sweep where present, confirming sufficient remaining oil volume to justify the drilling of new infill wells to develop these volumes during the 2019-2020 infill drilling campaigns.
The new generation PNL tool provides a low-cost alternative for effective reservoir depletion monitoring. Proper reservoir management, additional opportunity identification, and infill drilling target optimization are all benefits that can accrue from accurately locating bypassed oil. Field development plans can then be further optimized, resulting in increased asset value.
Machine learning has attracted the attention of geoscientists over the years. In particular, image analysis via machine learning has promise for application to exploration and production technologies. Demands have grown for the automation of carbonate lithology identification to shorten the delivery time of work and to enable unspecialized engineers to conduct it. The image analysis of carbonate thin sections is time consuming and requires expert knowledge of carbonate sedimentology. In this study, the authors propose an image analysis technique based on deep neural network for carbonate lithology identification of a thin section, which is an important image analysis process required for oil and gas exploration. In addition, the authors consider that porosity and permeability variations in the same facies are controlled by the grain, cement, pore, and limemud contents. If the contents are accurately measured, the porosity and permeability can be determined more accurately than by using traditional methods such as point counting. The elucidation of the complex relation of porosity and permeability is the objective of automation of carbonate lithology identification. To perform image analysis of the thin section, the authors prepared a data set mainly comprising pictures of the Pleistocene Ryukyu Group, which were composed of reef complex deposits distributed in southern Japan. The data set contains 306 thin section pictures and annotation data labeled by a carbonate sedimentologist. The rock components was divided into four types (grain, cement, pore, and limemud). A convolution neural network (CNN) was utilized to train the model. After training the neural network, each of the four categories was interpreted by the trained model automatically. Resultantly, the accuracy of automatic Dunham classification was 90.6% and the mean average test accuracy of category identification was 83.9%. The interpretation seems highly consistent between human vision and machine vision in both the overview and pixelwise scales. This result indicates that it has sufficient potential to assist geologists and become a basic tool for practical applications. However, the accuracy of category identification is still insufficient. The authors believe that the model requires higher quality supervised data and a greater number of supervised data.
Diagnostic fracture injection tests (DFIT) are conducted to estimate the magnitude of the minimum horizontal stress (tectonic) and characterize essential reservoir properties, such as reservoir permeability and actual reservoir pressure in conventional and unconventional reservoirs. When properly designed, and conducted, this type of transient test can help operators to reliably extract important reservoir data and reduce related operational costs and time. This paper provides a state of the art sensitivity analysis based on real pressure data that describes the impact of DFIT design on reservoir parameters acquisition.
In this study, the engineering steps to optimize the design, conduct the test and interpret acquired data are examined through a sensitivity analysis to obtain reliable results. Furthermore, the interpretations of the performed tests can be combined with an enhanced image log analysis (if available) to constrain the in-situ stress conditions, including the magnitude and direction for all three principal stress components.
Multiple operational parameters, such as injection rate, injection duration, rate reduction, leak-off mechanism and fall-off duration could significantly impact the fracture extent and mechanical response of the rock, thus affecting the fluid flow regime after shut-in. Therefore, all these variables should be evaluated in the proposed methodology to optimize the test, which is the key difference between conventional design and the presented reservoir driven design. To quantify the impact of operational parameters in reservoir response and validate the proposed approach, extensive sensitivities are performed with a complete well data set from a typical unconventional play by running in-house fracture models, considering multiple testing parameters (such as injection schedule, fluid type, leak-off, and net pressure analysis). Eventually, the optimal injection scenario can be determined, which could be applicable for regions with similar geological conditions.
This study demonstrates how uncertainties can be narrowed down when estimating the stress condition from fracture injection tests. The proposed approach can identify critical parameters and suggest best practices for diagnostic fracture tests under certain reservoir conditions. It can also be coupled with an enhanced image log analysis to fully determine the in-situ stresses magnitude and direction, which will increase the reliability of related geomechanical and reservoir analyses.
Mawlad, Arwa Ahmed (ADNOC Onshore) | Mohand, Richard (ADNOC) | Agnihotri, Praveen (ADNOC Onshore) | Pamungkas, Setiyo (ADNOC) | Omobude, Osemoahu (ADNOC) | Mustapha, Hussein (Schlumberger) | Freeman, Steve (Schlumberger) | Ghorayeb, Kassem (American University of Beirut) | Razouki, Ali (Schlumberger)
Challenges associated with volatile oil and gas prices and an enhanced emphasis on a cleaner energy world are pushing the oil and gas industry to re-consider its fundamental existing business-models and establish a long-term, more sustainable vision for the future. That vision needs to be more competitive, innovative, sustainable and profitable. To move along that path the oil and gas industry must proactively embrace the 4th Industrial Revolution (oil and gas 4.0) across every part of its business. This will help to overcome time constraints in the understanding and utilization of the terabytes of data that have been and are continuously being produced. There is a clear need to streamline and enhance the critical decision-making processes to deliver on key value drivers, reducing the cost per barrel, enabling greater efficiencies, enhanced sustainability and more predictable production.
Latest advances in software and hardware technologies enabled by virtually unlimited cloud compute and artificial intelligence (AI) capabilities are used to integrate the different petro-technical disciplines that feed into massive reservoir management programs. The presented work in this paper is the foundation of a future ADNOC digital reservoir management system that can power the business for the next several decades. In order to achieve that goal, we are integrating next generation data management systems, reservoir modeling workflows and AI assisted interpretation systems across all domains through the Intelligent Integrated Subsurface Modelling (IISM) program. The IISM is a multi-stage program, aimed at establishing a synergy between all domains including drilling, petrophysics, geology, geophysics, fluid modeling and reservoir engineering. A continuous feedback loop helps identify and deliver optimum solutions across the entire reservoir characterization and management workflow. The intent is to dramatically reduce the turnaround time, improve accuracy and understanding of the reservoir for better and more timely reservoir management decisions. This would ultimately make the management of the resources more efficient, agile and sustainable.
Data-driven machine learning (ML) workflows are currently being built across numerous petro-technical domains to enable quicker data processing, interpretation and insights from both structured and unstructured data. Automated quality controls and cross domain integration are integral to the system. This would ensure a better performance and deliver improvements in safety, efficiency and economics. This paper highlights how applying artificial intelligence, automation and cloud computing to complex reservoir management processes can transform a traditionally slow and disconnected set of processes into a near real time, fully integrated, workflow that can optimize efficiency, safety, performance and drive long term sustainability of the resource.
Estimating the lateral heterogeneity of geochemical properties of organic rich mudrocks is important for unconventional resource plays. Mature regions can rely on abundant well data to build empirical relationships and on traditional geostatistical methods to estimate properties between wells. However, well penetration in emerging plays are sparse and so these methods will not yield good results. In this case, quantitative seismic interpretation (QSI) might be helpful in estimating the desired properties. In this study, we use QSI based on a rock physics template in estimating the uncertainty of the geochemical properties of organic mudrocks of the Shublik Formation, North Slope, Alaska. A rock physics template incorporating lithology, pore fraction, kerogen fraction, and thermal maturity is constructed and validated using well data. The template clearly shows that the inversion problem is non-unique. Inverted impedances cubes are estimated from three seismic angle gathers (near with angles between 0° and 15°, mid with angle gathers between 15° and 30°, and far with angle gathers between 30° and 45°). The inversion is done using a model-based implementation with an initial earth model derived from the seismic velocity model used in the processing phase. By combining the rock physics template and the results of seismic inversion, multiple realizations of total organic content (TOC), matrix porosity, and brittleness index are generated. These parameters can be used for sweet spot detection. Lithological results can also be used as an input for basin and petroleum system modeling.