Hassan, Siti Normaizan (Petronas Carigali Sdn Bhd) | Bt Latif, Nurlizawati (Petronas Carigali Sdn Bhd) | Ashqar, Ayham (Petronas Carigali Sdn Bhd) | Ishak, Ahmad Zamzamie (Petronas Carigali Sdn Bhd) | Mustafa, Azfar Hafizin (Petronas Carigali Sdn Bhd) | Roy, Amit (Petronas Carigali Sdn Bhd)
This paper discusses a comprehensive study to address the uncertainties and troublesome major field development. The west Malaysia field has 7 platforms, and 110 wells. Production started in 1979. The highly complex, elongated anticline structure, possess over 330+ interpreted normal, reverse and wrench faults. It is deposited in Lower Coastal Plain within transgressive system tract.
Despite the strong and consistent reservoirs production, recovery Factor (RF) remained at 19% indicating potential remaining value.
The new look requirement were imposed as a result of disappointing drilling results mainly due to Key uncertainties in hydrocarbon redistribution, sand continuity and its quality, oil production across adjacent fault blocks.
Multicomponent seismic was acquired, to establish an updated reservoirs framework and assist in mapping hydrocarbon, water as well as lithology identification (sand/shale/coal).
Several wells were suspended based on the indicative study result. 85 new opportunities were identified, and tiered based on technical confidence and risk appetite. A roadmap consists of detailed development strategies spanning over 5 years and beyond associated with ~100MMstb reserves value that will bring the RF to 31% were proposed.
Main study outcomes resulted of reducing sand distribution uncertainty, reservoir extension was clarified and confirmed with the new seismic interpretation result. By passed oil were mapped.
Trezzi, Stefano (Schlumberger) | Glushchenko, Anna (Schlumberger) | Vasilyev, Pavel (Schlumberger) | Al Bulushi, Abdullah Mubarak Khamis (Schlumberger) | Cox, Edward (SNOC) | Al Hamadi, Masoud Ahmed (SNOC) | Stewart, Neil (SNOC)
This study summarizes the efforts taken to provide reliable structural delineations offshore the northern United Arab Emirates, an area where previous attempts failed to provide purposeful results.
With the employment of latest acquisition and processing techniques, a new high-density full azimuth volume shows clear uplift over legacy results. Structures never detected before are imaged, contributing to the de-risking of future well placement. These results are indicative of the challenges when acquiring and processing seismic data in the Northern Emirates.
The use of latest technologies was required to overcome several geophysical challenges, such as complex near surface, near-vertical thrust sequences and multiple faulting planes. All these elements contributed in generating extreme noise contamination, where a fast-varying geology with high dips made separation of primaries from noise one of the most difficult tasks.
Key pre-imaging technologies such as near surface characterization, 5D regularization to radial symmetry, as well as a tomographic velocity model building approach with iterative inputs from interpreters have been fundamental to converge to a solid velocity model that allows for a reliable structural imaging.
The work is particularly relevant for offshore exploration targets in the Northern Emirates, a region that has recently seen a growth in interest from local and international operators.
Low fold poorly sampled vintage seismic data often suffers from poor fault imaging. This can have a critical impact on reserve estimation and well planning. Acquiring high density seismic data over producing fields requires overcoming logistic challenges along with additional costs and increased acquisition time. However, advances in seismic processing technology could improve the fault resolution of vintage seismic data in a cost effective manner. This has been proven in a case study from offshore Abu Dhabi.
The presence of strong surface wave energy, resulting from the shallow water environment and near surface heterogeneity, masked events in the deeper part of the section. Poor and irregular spatial sampling caused aliasing of the surface wave. In the vintage processing, strong de-noising was applied to tackle the aliasing issue, which smeared the fault definitions. During the re-processing, a joint low-rank and sparse inversion was applied to regularize and densify the input data to obtain a de-aliased surface wave noise model. Subsequent adaptive subtraction of the noise from the input removed strong surface waves without damaging the body waves.
The stack quality was improved by application of cascaded surface wave attenuation algorithms. Additional five dimensional Fourier reconstructions of the data improved the signal quality. A carefully designed fault-preserving residual noise attenuation workflow further reduced the residual noise content. Automatic picking of key stratigraphic horizons was carried out in order to evaluate the spatial resolution of the re-processing outcome. Sharper discontinuities along fault planes observed compared to the interpretation of the vintage seismic data. Increased confidence in fault interpretation is of value for structural restoration study and further reservoir understanding. In addition, several new, previously not-visible, small fault features were highlighted as evident from volumetric curvature and semblance analysis. They have been effectively utilized in a forthcoming drilling campaign to de-risk well operation.
Multi-dimensional data densification to de-alias surface waves and five dimensional re-construction of the signal proved to be beneficial to enhance the fault features on the poorly sampled seismic data.
Normal Move-Out (NMO) velocity pick editing is the segregation of good and bad picks from an unsupervised auto-picking algorithm. As not all these picks are correct, manual velocity editing is required. This is time consuming, repetitive and typically requires a seismic expert for days to weeks. Automating it would require an algorithm that mimics the domain knowledge and expertise of a seismic processor; a deterministic approach would therefore likely fail. Alternatively, we propose a machine learning algorithm to identify valid time-velocity picks.
The proposed approach is a supervised classification approach which utilizes human interpreted velocity picks (1-5% of all picks) as training data. The algorithm learns to recognize the features of a valid velocity pick from metadata such as semblance energy, depth, areal location etc. and utilizes said understanding to segregate valid picks from invalid ones (multiples etc.) amongst the remaining velocity picks. The algorithm has been trained using synthetic NMO picks created by finite-difference forward modelling CMP data, including multiples, in the Marmousi model and auto-picking the move-out. The ground-truth NMO picks were created directly from the velocity model.
The trained classification neural network shows a very high > 97% accuracy on segregation of valid and invalid NMO velocity picks based on a 5% input data set. Further reduction of the training data set to 1% of velocity picks reduces test accuracy only by an additional 2 percentage points. Training and execution time of the neural network on a dataset of ~ 40000 velocity picks are also extremely fast (< 5 mins). Initial results on RMO picks also show a very similar performance characteristic.
The metadata for all valid picks spans a multi-dimensional feature space, from which the neural network constructs a non-linear selection criterion. A human can either manually QC each pick or perform attribute-based selection using only lower dimensional linear selection criteria. The robustness and speed of the neural network outperforms the manual editing while also reducing cycle time; the resulting velocity models will be superior, leading to improved signal processing and imaging results further in the processing sequence.
Automating velocity picking and editing has been a research objective for many years now, but only since the availability of modern computation and optimization algorithms can we properly deploy this to augment the high-quality modern velocity picking software and significantly decrease turn-around time by automating the picking and QC process.
In hydrocarbon exploration, seismic surface waves are used to characterize the near-surface by imaging the subsurface shear wave velocity for geo-hazard investigation and near surface seismic corrections to avoid false structures in the final seismic image. Surface waves, identified in a conventional surface acquisition experiment, can be analyzed in the frequency wavenumber (FK) domain to generate dispersion curves at each shot location. The subsurface shear wave velocity is represented as a 1D profile with lateral variations can be handled using laterally constrained inversion or by applying spatial interpolation of 1D results. We identify two fundamental challenges to perform surface wave analysis. First, inadequate sampling of the surface wave in conventional sensor arrays may create artifacts in the frequency-wavenumber domain, which introduces further distortion in the signal. The use of broadband single-sensor single source land 3D seismic data provides adequate sampling of surface wave energy that is captured with negligible aliasing and high signal power. This makes it possible to record fundamental and higher surface wave modes at large frequencies. Second, it is common in seismic exploration to deal with large amounts of seismic data on several tens of thousands shot gathers of the single sensor survey making manual picking of dispersion curves a tedious and time-consuming job. We developed a deep belief network (DBN) with multiple hidden layers to pick fundamental modes in the phase velocity spectrum. The neural network workflow was trained on 1500 gathers and validated on several 100 gathers. Finally, the automated picking was applied to roughly 50,000 gathers using frequency range (3-30 Hz). The resulting dispersion curves show high spatial correlation and are geologically consistent. The fundamental mode pseudo-section shows smooth changes with significant lateral variations of Rayleigh-wave phase velocities. The second and third mode of dispersion curves are observed in some shots in the F-K spectrum but usually they have weaker energy than fundamental mode. The recent advances in surface wave analysis is presented over a complex structure where the raw data are characterized by strong Rayleigh waves dominated by a fundamental mode. Dispersion curves were inverted using nonlinear conjugate gradients to generate a shear wave velocity model with high vertical resolution for the first 50 m depth. The recent development in seismic data acquisition using single sensor broad band data, and advances in seismic processing using deep neural network lead to a novel technology that enable automatic picking of dispersion curves.
A state-of-the-art, high-density full azimuth seismic dataset was acquired over a producing field southwest Abu Dhabi to determine optimum high-resolution acquisition and processing parameters. The existing 3D seismic data was acquired in 1994-95 with low fold coverage. The central part of the new seismic volume has good well control to develop, optimize, and QC processing workflows. The new dataset utilised the system with single-sensor geophone accelerometers, point source DX80 vibrators using the Maximum Displacement sweep design with an enhancement of low frequencies as such a broadened amplitude spectrum. The key specifications for seismic acquisition include full azimuth high-density single-source / single-sensor data, long offsets and a broadband vibroseis sweep that retains low frequencies down to 3Hz. The single-sensor single-source technology and the full azimuth dense geometry (5x5m grid) was designed for an optimum subsurface imaging for this survey. The volume was processed through anisotropic prestack time and depth migration followed by poststack wavelet processing and spectral balancing.
High fidelity seismic amplitude reconstruction through pre-stack migration is crucial for accurate elastic inversion. Despite a relatively flat geology of the Abu Dhabi region, accurate imaging is required for a stable elastic inversion. This can be challenging because the main reservoir Arab lies underneath the strongly anisotropic overburden of the Nahr Umr formation. In this case study, we show how we effectively addressed this challenge through PSDM.
With PSDM imaging, we have overcome the challenges of complex ray paths passing through the strongly anisotropic Nahr Umr layer and the rapid lateral velocity variation in the Mishrif formation. Evidently, the success of PSDM relies strongly on the accuracy of the depth velocity model used. To achieve this we adopt different forms of tomographic inversion, for example, using 3D non-linear slope tomographic inversion, where velocity and anisotropy (Epsilon) models are jointly inverted. Additionally, short wavelength velocity variations caused by the Mishrif interval are resolved through structurally-constrained tomography (SCT).
The superiority of PSDM imaging over PSTM in reconstructing AVA compliant seismic amplitudes is demonstrated on an ocean bottom survey from the transition zone offshore Abu Dhabi. Fast-track AVA elastic inversion is used to assess the benefit of PSDM imaging over PSTM. With a more stable Vp/Vs ratio and smaller inversion residual, PSDM imaging demonstrates a greater accuracy in reconstructing the pre-stack seismic amplitude and thus are more appropriate for estimating elastic reservoir properties.
The value of PSDM imaging for better understanding of reservoir characteristic has been well demonstrated in this case study from the Abu Dhabi transition zone, thus optimizing the value of the acquired seismic data for asset development.
Ghazali, Ahmad Riza (PETRONAS) | Abdul Rahim, M. Faizal (PETRONAS) | Mad Zahir, M. Hafizal (PETRONAS) | Muhammad, M. Daniel Davis (PETRONAS) | Mohammad, M. Afzan (PETRONAS) | A. Aziz, Khairul Mustaqim (PETRONAS)
The key objectives were to achieve better seismic resolution and spatial delineation in very heterogeneous reservoirs. We decided to supplement simultaneously the surface 3D multi component seismic acquisition by placing additional fiber optic live receivers in the subsurface via a "True-3D" experiment without shutting down the oil production. The most cost-effective method to snapshot this wavefield propagation downhole is by utilizing fiber optic Distributed Acoustic Sensing (DAS). The borehole 3D VSP data were acquired by sharing the surface OBN nodal survey airgun sources. This is an important experiment for the field in the future so that the need to halt insitu field production for 4D time lapse monitoring will not be required if the S/N is acceptable by using this method. This permanent installation of fiber optic cables has become our ears on wells, not only for 3D DAS VSP but for proactive monitoring of the field, ensuring optimum production performance throughout the life of the field.
This study examines which is the margin of usability for Artificial Intelligence (AI) algorithms related to the rock properties distribution in static modeling. This novel method shows a forward modeling approach using neural networks and genetic algorithms to optimize correlation patterns among seismic traces of stack volumes and well rock properties. Once a set of nonlinear functions is optimized in the well locations, to correlate seismic traces and rock properties, spatial response is estimated using the seismic volume. This seismic characterization process is directly dependent on the error minimization during the structural seismic interpretation process, as well as, honoring the structural complexity while modeling. Previous points are key elements to obtain an adequate correlation between well data and seismic traces. The joint mechanism of neural networks and genetic algorithms globally optimize the nonlinear functions and its parameters to minimize the cost function. Estimated objective function correlates well rock properties with seismic stack data. This mechanism is applied to real data, within a high structural complexity and several wells. As an output, calibrated petrophysical time volumes in the interval of interest are obtained. Properties are used initially to generate a geological facies model. Subsequently, facies and seismic properties are used for the three-dimensional distribution of petrophysical properties such as: rock type, porosity, clay volume and permeability. Therefore, artificial intelligence algorithms can be widely exploited for uncertainty reduction within the rock property spatial estimation.
Despite the now-routine use of prestack depth migration (PSDM) for unconventionals, confusion abounds on the topic of how to best incorporate near-surface velocity estimates into the PSDM shallow-model-building process. The present work seeks to eliminate the confusion via a carefully-controlled synthetic experiment in which the (known) near-surface velocity distribution mimics typical Permian Basin shallow geology. In this experiment, various methods for near-surface model building are tested, ranging from simplistic to sophisticated, and PSDM results are compared against the ideal image. These tests clearly demonstrate that gather flattening improves dramatically with application of the more sophisticated shallow model building approaches. In the case of the most primitive approaches (e.g, migration-from-flat-datum or migration from topography where the shallow velocity cells are flooded with a spatially uniform “replacement” velocity), the migrated gathers exhibit significant residual moveout, and applying a tomographic velocity update to improve flattening leads to a significant error in event depth location (i.e, “depthing”). This depthing error suggests that downstream anisotropic parameter estimation will be compromised unless a more sophisticated shallow model building approach is employed. The concept of differential statics is introduced and is demonstrated to be a useful tool which can provide good gather flattening, accurate event depthing, and also improved lateral continuity of events in the common case where the near-surface velocity estimate from refraction statics analysis is not suitable for verbatim insertion into the shallow PSDM model. Key findings from the synthetic experiments are corroborated by analogous observations on real data, suggesting that the experiments are indeed capturing realistic effects.
It is well known that near-surface heterogeneity can cause significant traveltime distortion of reflected signals, and, furthermore, that such distortion poses a major challenge in land seismic imaging. Addressing this challenge is particularly important in unconventional plays, where accurate depthing of subtle features is crucial for applications such as landing and steering optimization. Recently, some notable advances have been made, including the use of novel refraction statics techniques (Diggins et al., 2016), application of full-waveform inversion (e.g., Roy et al., 2017), and incorporation of gravity/EM data (Colombo et al., 2012), all of which seek to better estimate the near-surface velocity field. At the same time as these advances are unfolding, prestack depth migration is beginning to see widespread use in many North American unconventional shale plays (Rauch-Davies et al., 2018).