Deepwater reservoirs often consist of highly laminated sand-shale sequences, where the formation layers are too thin to be resolved by conventional logging tools. To better estimate net sand and hydrocarbon volume in place, one may need to leverage the high resolutions offered by borehole image logs. Traditionally, explicit sand counting in thin beds has been done by applying a user-specified cutoff on a 1D resistivity curve extracted from electrical borehole images. These workflows require multiple preprocessing steps and log calibration, and the results are often highly sensitive to the cutoff selection, especially in high-salinity environments.
This paper presents a new method that estimates sand fractions directly from electrical borehole images without extracting an image resistivity curve or applying any preselected cutoffs. The processing is based on an artificial neural network, which takes the 2D borehole image array as input, and predicts sand fractions with the measurements from all button electrodes. A cumulative sand count can be computed after processing the borehole image logs along an entire well by summing up the estimated net sands. The neural network is trained and tested on a large dataset from wells in a deepwater reservoir with various degrees of laminations, and validated with sand fractions identified from core photos. Upon testing, a good match has been observed between the prediction and the target output. The results were also compared against another sand-counting method based on texture analysis, and showed advantages of yielding unbiased estimations and a lower margin of error.
Conceptual limitations of existing gridding technologies often lead to undesirable simplifications to the modeling of structurally complex areas, and consequently poor predictions. We present a structural modeling and gridding workflow that limits these modeling compromises.
A volume-based 3D structural model based on fault and horizon surfaces is constructed from input data that has undergone basic quality checking using a variety of techniques. The critical step in the grid creation is the definition of a flattened (‘depositional’) space that deforms the structural model mesh under mechanical constraints. A 3D ‘unstructured’ grid is created in the depositional space, based on ‘cutting’ a property-populated, regular cuboidal grid by the geological discontinuities. The tectonic consistency and better preservation of geodetic distance make the flattened space ideal for a range of property modeling approaches. The forward-deformation of the grid into true geological space tends to preserve the layer-orthogonality of the grid columns and makes the grid more suited to numerical simulation approximations. The final grid is unstructured, high quality and an accurate representation of the input structural model.
The 3D structural model, depositional space transform and grid geometries all provide valuable information on the structural quality of the input data. The stretching and deforming of the orthogonal local axes in the transformation from depositional space to geological space are used to focus further effort on structural model quality assurance (QA). The key step in generating accurate property population and simulation models is the application of QA metrics on the grid geometry; the transformation from depositional space to geological space is used to generate a set of grid properties that highlight potential structural inconsistencies or data quality issues back in the structural model. We present several examples based on a range of structurally complex models, and demonstrate the downstream impact of applying this QA workflow throughout the stages of input data validation, structural model creation and grid creation.
Commencement of initial field production is a unique opportunity to acquire reservoir surveillance information that can inform future reservoir performance. When a field is perturbed from original conditions with first production, there is potential for reservoir property uncertainty reduction by observing pressure measurements at non-producing wells with downhole pressure gauges and comparing the observed signal to a range of simulation model results.
The Wheatstone field, located offshore northwest Australia, has recently commenced production start-up to supply gas to the Wheatstone LNG facility. The operational guidelines required each development well to commence with a single well cleanup flow to the Wheatstone platform. The initial single well cleanup flows of the Wheatstone field allowed scope for the selection of a well flow sequence with observation at non-producing wells.
The recommended sequence of initial cleanup flows was designed with a focus on reducing reservoir uncertainties via the use of Ensemble Variance Analysis (EVA). EVA is a statistical correlation technique which compares the co-variance between two sets of output data with the same set of inputs. For the Wheatstone field well cleanup flow sequence selection, the EVA workflow compared the full field Design of Experiments (DoE) study of field depletion and a series of short early production reservoir simulation DoE studies of the gas field. The co-variance between the two DoE studies was evaluated. The objective of the EVA approach was to determine the startup sequence that would allow for the best opportunity for subsurface uncertainty reduction. This objective was met by ranking multiple cleanup flow sequence scenarios. The key factors considered for sequence selection ranking were the impact on business objectives such as future drilling campaign timing and location of infill wells, as well as insights on reservoir connectivity, gas initially in place and permeability.
The recommended sequence of well cleanup flows uses super-positioning of pressure signal to boost response at observation wells, which improves measurement resolvability. The selected sequence preserves key observation wells for each manifold and reservoir section for as long as possible before those wells were required to be flowed to meet operational requirements. Operational constraints and variations of the startup plan were considered as part of the evaluation.
In an era of automated workflow-assisted dynamic modelling, Special Core Analysis (SCAL) parameters require updating for each static realisation and evaluation at a quantifiable, probabilistic level-of-certainty. Additionally, SCAL data gaps combined with limited reliable SCAL data drive the need to establish trends and correlations from analogues.
SCAL parameters from analogue fields were selected and filtered by depositional environment and laboratory experiment type (centrifuge versus displacement). These analogue SCAL parameters were allocated to statistical bins defined by absolute permeability ranges. Statistical analysis of each SCAL parameter allocated to each permeability bin produced a probability distribution discretised by percentile. Multi-variable linear regression (MVLR) was then implemented to correlate each SCAL parameter, as the response variable, to input variables absolute permeability and percentile. SCAL correlations of reasonable to excellent quality were obtained.
The depositional environment was of second order influence in establishing these SCAL correlations. This was due to the selection of core plugs for laboratory analysis from layers of similar quality irrespective of the depositional environment, highlighting the need to select samples characterising a range of lithology and reservoir quality. Centrifuge experiments of water displacing gas were discarded as unreliable due to the compression of the gas phase by the experimental technique.
The multi-variable linear regression methodology enabled SCAL parameters to be determined as a function of both absolute permeability and probability. This approach should enable an automated implementation of SCAL parameters within each dynamic model realisation.
This paper presents case studies on reservoir and well management of two laterally and vertically compartmentalized Western Australian Triassic gas condensate reservoirs, developed by five multi-zone "smart" wells with sand control, tied back to an offshore platform via a subsea network. In managing assets with such complexity, it is imperative to understand reservoir performance on a zone-by-zone basis. Quantifying performance allows management of flux through downhole sand control systems and optimisation of offtake strategy. The majority of the material published to date on "smart" wells has been focused on completion design optimisation and minimisation of unwanted oil/water production. There are few existing articles about production and reservoir optimisation of high rate gas wells requiring flux management.
This paper showcases how remotely-operated selective completions ("smart" wells with permanent downhole gauges for each completion coupled with subsea flow meters for each well) have been instrumental in facilitating prompt analysis of zonal reservoir performance and thus in yielding insights into reservoir connectivity and allowing optimisation of zonal contributions. Various case studies will be presented showing how reservoir surveillance data is acquired and interpreted to optimize well zone-by-zone production and to manage flux limits on each producing zone. These case studies will include manipulation of downhole valves to provide information for established techniques such as interference testing and P/Z analysis.
Data acquisition and interpretation challenges are highlighted along with fit-for-purpose solutions developed to overcome those challenges.
The insights presented could facilitate better planning of similar systems in the future.
Lacaze, Sébastien (Eliis SAS. Parc Mermoz, Immeuble l’Onyx, 187 rue Hélène Boucher, 34170 Castelnau-Le-Lez, France) | Philit, Sven (Eliis SAS. Parc Mermoz, Immeuble l’Onyx, 187 rue Hélène Boucher, 34170 Castelnau-Le-Lez, France) | Pauget, Fabien (Eliis SAS. Parc Mermoz, Immeuble l’Onyx, 187 rue Hélène Boucher, 34170 Castelnau-Le-Lez, France) | Wilson, Thomas (Eliis Pty Ltd, 191 St Georges Terrace, Perth WA 6000, Australia)
Summary Structural interpretation from 2D seismic line sets is common in the hydrocarbon exploration process. Generally, this process consists in the interpretation of several key horizons on the several seismic lines available, which is time-demanding. Furthermore, as the seismic lines are often processed differently, misties are frequently observed between the lines, which may lead to cumbersome interpretation. In this paper, we introduce a method aiming at providing an efficient 2D line seismic interpretation answering the need of obtaining early 3D geological model during reservoir exploration. Introduction Traditional 2D seismic interpretation is generally a complex and time-consuming task which relies on 2D autotracking and the manual picking of a few stratigraphic events on every line.
Estimation of scattering and intrinsic attenuation factors from seismic observations is of interest for subsurface imaging and characterization. We discuss a waveform inversion (WI) method for zero-offset seismic borehole data that explicitly models interference and multiple scattering in layered media using well logs. On the synthetic example, we show capability of WI to discriminate between scattering and intrinsic Q in 1D vertically inhomogeneous media. Depending on the scale of the supplied logs, the inverted Q-factors correspond either to the ‘effective’ attenuation or solely to the intrinsic absorption in rocks. We apply the WI to the nearly zero-offset VSP dataset acquired in the Nephrite-8 well (Cooper Basin, Australia). The borehole intersects the Patchawarra formation characterized by high-contrast interlayering of coal seams. For this formation, estimated intrinsic attenuation 1/Qint ≈ 0.014 is negligible compared to the stratigraphic filtering 1/Qscat ≈ 0.121±0.02. These 1/Qscat estimates are somewhat higher than those obtained by the application of the generalized O’Doherty - Anstey theory.
Presentation Date: Wednesday, October 17, 2018
Start Time: 8:30:00 AM
Location: 212A (Anaheim Convention Center)
Presentation Type: Oral
Beloborodov, Roman (CSIRO, Kensington, WA, Australia) | Pervukhina, Marina (CSIRO, Kensington, WA, Australia) | Shulakova, Valeriya (CSIRO, Kensington, WA, Australia) | Josh, Matthew (CSIRO, Kensington, WA, Australia) | Hauser, Juerg (CSIRO, Kensington, WA, Australia) | Clennell, Michael B. (CSIRO, Kensington, WA, Australia) | Chagalov, Dimitri (ExxonMobil, Melbourne, VIC, Australia)
Shales are omnipresent in sedimentary basins and generally need to be drilled through to reach conventional or to develop unconventional reservoir. Shales, especially smectite-rich, often cause significant drilling problems associated with overpressure, borehole instability, etc. Understanding of clay mineralogy before drilling is very important to reduce risks associated with drilling. In this study, we perform a simultaneous AVO inversion of a part of the Duyfken seismic survey, the Northern Carnarvon Basin of the North-West Shelf of Australia. Log data from a training well were used to establish correlations between smectite content and acoustic impedance (AI) and VP/VS ratio. It is worth noting that mechanically and chemically compacted shale exhibit two significantly different trends between smectite and a principal component of seismic attributes. The smectite content obtained from surface seismic is in a good agreement with that estimated in a blind test well from the XRD analysis of cuttings.
Presentation Date: Tuesday, October 16, 2018
Start Time: 1:50:00 PM
Location: 210A (Anaheim Convention Center)
Presentation Type: Oral
Summary Concepts for the relation between faults and folds in an extensional regime have revealed the importance of detailed mapping of fault zones, particularly in combined structural-stratigraphic traps. We show in a case study from the Australian North West Shelf that extensional fault zones are composed of several individual faults. Depending on the depth within the faulted interval, the individual faults may be interrupted by relay ramps. Additionally, release faults perpendicular to the main faults may introduce further potential breaches of the fault seal. Understanding the faults within the reservoir at this level of detail will assist in better understanding the fault seal risk for combined structural-stratigraphic traps.
O'Reilly, Daniel I. (Chevron Australia and University of Adelaide) | Hopcroft, Brad S. (Chevron Australia) | Nelligan, Kate A. (Chevron Australia) | Ng, Guan K. (Chevron Australia) | Goff, Bree H. (Chevron Australia) | Haghighi, Manouchehr (University of Adelaide)
Barrow Island (BWI), 56 km from the coast of Western Australia (WA), is home to several mature reservoirs that have produced oil since 1965. The main reservoir is the Windalia Sandstone, and it has been waterflooded since 1967, whereas all the other reservoirs are under primary depletion. Because of the maturity of the asset, it is economically critical to continue to maximize oil-production rates from the 430 online, artificially lifted wells. It is not an easy task to rank well-stimulation opportunities and streamline their execution. To this end, the BWI Subsurface Team applied the Lean Sigma processes to identify opportunities, increase efficiency, and reduce waste relating to well stimulation and well-performance improvement.
The Lean Sigma methodology is a combination of Lean Production and Six Sigma, which are methods used to minimize waste and reduce variability, respectively. The methods are used globally in many industries, especially those involved in manufacturing. In this asset, we applied the processes specifically to well-performance improvement through stimulation and other means. The team broadly focused on categorizing opportunities in both production and injection wells and ranking them—specifically, descaling wells, matrix acidizing, sucker-rod optimization, reperforating, and proactive workovers. The process for performing each type of job was mapped, and bottlenecks in each process were isolated.
Upon entering the “control” phase, several opportunities had been identified and put in place. Substantial improvements were made to the procurement, logistics, and storage of hydrochloric acid (HCl) and associated additives, enabling quicker execution of stimulation work. A new program was also developed to stimulate wells that had recently failed and were already awaiting workover (AWO), which reduced costs. A database containing the stimulation opportunities available at each individual well assisted with this process. The project resulted in the stimulation of several wells in the asset, with sizable oil-rate increases in each.
This case study will extend the information available within the oil-industry literature regarding the application of Lean Sigma to producing assets. It will assist other operators when evaluating well-stimulation opportunities in their fields. Technical information will be shared regarding feasibility studies (laboratory-compatibility work and well-transient-testing results) for acid stimulation and steps that can be taken to streamline the execution of such work. Some insights will also be shared regarding the most-efficient manner to plan rig work regarding stimulation workovers.