Shell (Brazil)

ABSTRACT As the operator of several exploratory blocks in ultradeep waters, Petrobras was responsible for many presalt oil discoveries in Santos Basin such as Tupi, Carioca, Guará, and Iara. In partnership with the National Petroleum, Natural Gas and Biofuels Agency (ANP), Petrobras drilled well 2-ANP-2A, which resulted in the Libra discovery. In 2013, Libra was offered in the first bidding round executed by the Brazilian government under the new Production Sharing Contract for presalt areas. The winning consortium is comprised of Petrobras (operator), Shell, Total, CNOOC (China National Offshore Oil Corporation), CNPC (China National Petroleum Corporation), and PPSA (Pré-Sal Petróleo S.A.). The Libra discovery is sitting over a structural trap of about 550 km2 (212 mi2) closure at the Aptian top reservoirs level presenting a maximum oil column that can reach up to 900 m (2953 ft). The main reservoirs are lacustrine carbonates, deposited from the Neobarremian until the Aptian. Preliminary estimates indicate a volume of oil in place between 8 and 12 billion BOE. The development proposed for Libra started with Phase 0, in 2014, and is focused on information gathering, including appraisal wells, extended well tests (EWT), early production systems (EPS), and a pilot project. Phase 1 encompasses the definitive production systems and is expected to start in 2022 and finish in 2030.

Com o avanço da tecnologia da informação e da comunicação e as mudanças da sociedade, novas formas de flexibilização das relações de trabalho têm se disseminado, entre as quais o teletrabalho. Uma das modalidades de teletrabalho é aquela em que o indivíduo executa as tarefas de casa, mantendo, porém, o vínculo de emprego formal com uma organização. Este artigo focaliza, em especial, essa modalidade, levantando as percepções dos indivíduos que nela atuam sobre as consequências para a sua vida pessoal e profissional. Trata-se de estudo de caso na Shell Brasil, empresa que, desde 2000, promoveu a migração de alguns funcionários para o regime home-office. A metodologia de natureza qualitativa compreendeu entrevistas com profissionais desse grupo. Os resultados permitiram identificar um quadro conceitual que aponta elementos condicionantes e elementos que os indivíduos parecem utilizar como balizadores de suas avaliações sobre a condição de teletrabalhadores home-office. Entre os condicionantes, destacam-se características da pessoa, da organização, dos recursos e procedimentos disponibilizados, do trabalho, da família, do espaço doméstico e da sociedade. Como elementos balizadores surgem: o desempenho na empresa e os efeitos para a carreira, a relação com o trabalho, a vida pessoal e as relações sociais e em família.

Summary It is extremely challenging to design effective assisted-history-matching (AHM) methods for complex geological models with discrete-facies types. One of the difficulties is the irregular and nonsmooth nature of the data-mismatch function that needs to be minimized, because of either numerical noise on simulation results or nonsmooth reparameterization. In this paper, a parallelized direct-pattern-search (DPS) approach with auto-adaptive pattern-size updating is developed to guarantee the convergence of the data-mismatch minimization, even when the objective function is nonsmooth because of numerical noise. A trust-region variant of the Gauss-Newton (GN) or quasi-Newton (QN) method is effectively combined with the noise-insensitive DPS method to enhance its performance by exploiting any available smoothness features of the objective function. The new approach is first validated by a linear toy problem and a nonlinear toy problem where artificial numerical noise is introduced. Then, it is applied to a synthetic case and a real field case for history matching of channelized-turbidite reservoirs with three facies types. The model parameters subject to AHM include principal component analysis (PCA) coefficients, which automatically reconstruct the facies indicators and permeability, porosity, and net-to-gross maps. Other matching parameters such as aquifer strength and fault transmissibility are also included. Numerical tests indicate that the hybrid algorithms perform better than the traditional QN line-search algorithms and the original Hooke-Jeeves DPS algorithm (Hooke and Jeeves 1961). The hybrid algorithms either can converge to a satisfactory solution with the same accuracy using lower cost or find a better solution with the same cost, especially for cases where adjoint derivatives are unavailable and numerical noise is unavoidable from reservoir simulation. The GN-DPS algorithm performs the best among all tested algorithms. The history-matched reservoir models obtained with the new AHM approach (GN-DPS combined with pluri-PCA) honor the production measurements with good accuracy. For both the synthetic and real cases, the history-matched reservoir models preserve geological realism.

Summary Assisted history matching (AHM) of a channelized reservoir is still a very-challenging task because it is very difficult to gradually deform the discrete facies in an automated fashion, while preserving geological realism. In this paper, a pluri-principal-component-analysis (PCA) method, which supports PCA with a pluri-Gaussian model, is proposed to reconstruct geological and reservoir models with multiple facies. PCA extracts the major geological features from a large collection of training channelized models and generates gridblock-based properties and real-valued (i.e., noninteger-valued) facies. The real-valued facies are mapped to discrete facies indicators according to rock-type rules (RTRs) that determine the fraction of each facies and neighboring connections between different facies. Pluri-PCA preserves the main (or principal) features of both geological and geostatistical characteristics of the prior models. A new method is also proposed to automatically build the RTRs with an ensemble of training realizations. An AHM work flow is developed by integrating pluri-PCA with a derivative-free optimization algorithm. This work flow is validated on a synthetic model with four facies types and a real-field channelized model with three facies types, and it is applied to update both the facies model and the reservoir model by conditioning to production data and/or hard data. The models generated by pluri-PCA preserve the major geological/geostatistical descriptions of the original training models. This has great potential for practical applications in large-scale history matching and uncertainty quantification.

Abstract The Salema field, a waterflood development off the coast of Brazil (Campos Basin) has been suffering from early water breakthrough and poor sweep on the south part of the field. In an attempt to increase the sweep efficiency, a novel in-depth waterflood conformance control technology commercialized by Tiorco (Nalco company) as Brightwater®6 has been selected. This paper describes the feasibility, maturation, execution, monitoring and results of the first trial in Brazil (July 2009), which is also the first for Shell worldwide. We will discuss reservoir simulation and lab work (core tests, sand-pack tests…) that lead to the decision of performing the field trial. The offshore set-up and pumping job will be described briefly. Finally surveillance techniques and results to date will be presented.

Abstract Regional and detailed seismic stratigraphic analyses of Early Cretaceous (Aptian) presalt carbonate sections from offshore Brazil reveal the complex stratigraphic architecture of late- and post-rift lacustrine carbonate systems. The lateral and vertical distribution of calibrated seismic facies within this framework highlights the evolution through time of the carbonate system and bathymetry of the host lacustrine basin. Despite the simple, largely abiotic and microbial components, lacustrine carbonate accumulations formed complex geometries that closely resemble those observed from marine systems, suggesting that a downward-tapering carbonate production profile must have occurred. The complexity of the stratigraphic architecture reflects lateral variations in subsidence patterns combined with the interference of the basement topography, palaeowind directions and basinal filling patterns. Well-imaged clinoforms several hundred metres high attest to both the existence of significant lake-bottom topography, locally in excess of 800 m, and the occurrence of deep water at time of deposition. Platform margin trajectory and vertical and lateral architecture of clinoform packages through time reveal distinct sequence boundaries that can be correlated in detail only locally, demonstrating the impact of syndepositional tectonics, and possibly recurrent isolation of smaller lakes during lowstands. Depositional models from this study fill a gap in current understanding of lacustrine carbonate systems and offer a template for exploration and appraisal of the presalt play.

Electric Submersible Pump (ESP) account for over 60% of artificial lift methods used globally and contribute significantly to the CAPEX and OPEX of a project. They tend to be the least reliable component in the system with an average life-span of 2 years. This paper demonstrates how artificial intelligence was used to unlock insights from sensor data around an ESP to understand the operating conditions which lead to a trip and failure of these systems. Autoencoders were used for the detection of anomalous behavior in an ESP and the determination of the root cause of an anomalous event. Autoencoders are neural networks trained to reconstruct input data. They have an encoding and decoding section, the encoder compresses the input vector, while the decoder reconstructs the original input from the compressed vector. This process allows the network to understand the patterns in a dataset. We trained the network on stable operating data from a 2-years historical data dump of 97 sensors. This allowed the model to understand the patterns of stability in an ESP. The autoencoder was developed using the Python programming language along with the Keras deep learning framework. It had 7 layers with the exponential linear unit as the activation function for training. During reconstruction, the autoencoder never produces a perfect reconstruction of input data, it, however, performs a good reconstruction on data similar to what it was trained on. In our case, the model reconstructs stable data well and struggles with unstable data. The reconstruction error is used to distinguish a normal event from an anomalous event because it increases prior to an event and reduces as the system returns to stability. During the historical time period, the ESP experienced 5 major trips, three of them were due to gas locks while the other two were due to electrical issues. The model was able to detect the gas locks on average 5 hrs in advance and electrical issues several days in advance before the actual events. The top ten sensors responsible for each event were determined based on the relative magnitude of the individual sensor reconstruction errors, the validity of this output was confirmed by the Subject Matter Expert. Autoencoders can make non-linear correlation between features in a dataset and have been used for anomaly detection in images and other fields, this paper demonstrates their usefulness in intelligent surveillance of ESPs. This solution is currently used for near real-time intelligent surveillance of ESPs with the ability to send out email notifications whenever any sensor strays away from stability.

Abstract This paper describes field experience and lessons learned from scale-control operations in a deepwater subsea development in the Campos Basin, Brazil; specifically, from bullheading scale-inhibitor squeezes from the FPSO host, along the production flowlines, into four low-watercut, horizontal subsea wells, completed with sand control. The relatively small number of high-cost, highly productive wells, coupled with a very high barium-sulfate scaling tendency upon breakthrough of injection water, meant not only was effective scale management critical to achieve high hydrocarbon recovery, but even wells at low water cuts were deemed to be at sufficient risk to require squeeze application. Use of conventional, water-based squeezes have been known to cause significant damage to productivity in low-watercut wells, including those showing a fines-migration tendency, as was the case here. Hence, based upon risk mitigation, supported by an extensive programme of laboratory testing, it was decided that for the initial treatments only the mainflush would be water-based, with a mutual-solvent preflush and marine-diesel overflush. Other key challenges associated with treating from the host included the remote location of the wells, the potential to form hydrates, the cleanliness of the lines along which the treatment would pass, the achievement of effective placement over a long producing interval, as well as the need to deploy the chemical package via a support vessel adjacent to the FPSO. All had to be managed due to the high cost and low availability of a deepwater rig that could deploy the treatments directly to the subsea wellheads. The paper will explore in detail the issues associated with inhibitor-squeeze deployment in deepwater, subsea fields, many of which are currently being developed in the Campos basin, Gulf of Mexico and West Africa, and is a good example of best-practice sharing from another oil basin.

Summary This paper describes field experience and lessons learned from scale control operations in a deepwater subsea development in the Campos Basin, Brazil; specifically, from bullheading scale-inhibitor squeezes from the FPSO host, along the production flowlines, into four low-watercut, horizontal subsea wells, completed with sand control. The relatively small number of high-cost, highly productive wells, coupled with a very high barium-sulfate scaling tendency upon breakthrough of injection water, meant that not only was effective scale management critical to achieve high hydrocarbon recovery, but even wells at low water cuts were deemed to be at sufficient risk to require squeeze application. Use of conventional, water based squeezes have been known to cause significant damage to productivity in low-watercut wells, including those showing a fines-migration tendency, as was the case here. Hence, on the basis of risk mitigation, supported by an extensive program of laboratory testing, it was decided that for the initial treatments, only the mainflush would be water based, with a mutual-solvent preflush and marine-diesel overflush. Other key challenges associated with treating from the host included the remote location of the wells, the potential to form hydrates, the cleanliness of the lines along which the treatment would pass, the achievement of effective placement over a long producing interval, as well as the need to deploy the chemical package via a support vessel adjacent to the FPSO. All had to be managed because of the high cost and low availability of a deepwater rig that could deploy the treatments directly to the subsea wellheads. This paper will explore in detail the issues associated with inhibitor-squeeze deployment in deepwater, subsea fields, many of which are currently being developed in the Campos basin, Gulf of Mexico, and West Africa, and are a good example of best-practice sharing from another oil basin.

Abstract It is a common practice to reduce the number of parameters that are used to fully describe a static geological model for assisted-history-matching (AHM) of geologically complex reservoirs. However, a model reconstructed from the reduced parameters may often be distorted from prior geological information, especially when discrete facies indicator presents in the model; for example, a reconstructed “channel” does not look like a channel. This paper presents a novel machine learning (ML) method that learns prior geological information/data, and then reconstructs a model after pluri-principal-component-analysis (pluri-PCA) is applied. The main steps of the methods are: first, a dictionary of object-based channelized geological models is generated based on the prior geological data/information. A pluri-PCA approach is applied to reduce the dimensions of grid-based static model and to convert the facies models to Gaussian PCA-coefficients. Second, the PCA coefficients are tuned during history matching process and the pluri-Gaussian rock-type-rule is applied to reconstruct the complex geological facies model from the tuned coefficients. Finally, a ML technique called “Piecewise Reconstruction from a Dictionary” (PRaD), which is based on the Markov Random Field method, is introduced to minimize the feature distance between the reconstructed model and the training models. In order to enforce geological plausibility, the facies models are reconstructed or regenerated by putting together pieces from different patches in the training realizations. An AHM workflow with the above described new method has been applied to a real turbidite channelized reservoir. The prior geological model indicates that there is clear sand deposition between a gas injector and oil producers. However, one of the production wells has been observed much less gas production than simulated result. Without adding the plausiable additonal fault, the AHM results convinced that the reasonable match on gas production can only be achieved by changing channel orientation and shales/facies distribution. In addition, the new method is observed to preserve both channel features and geostatistics of the model parameters (e.g. facies, permeability, porosity). The additional uncertainties in dynamic aspects (e.g. aquifer strength, relative permeability multipliers, etc.) will be included in AHM workflow and addressed by a derivative-free optimization approach. The new method is able to leverage the prior information provided by geologists in order to produce a non-Gaussian geologically plausible facies model that matches the observation data. While the pluri-PCA reconstruction process helps to preserve the major features and facies fraction within the geological model description, the PRaD method recaptures the missing details of minor features and enables the final model to closely link to the training realizations. Unlike the conventional approach, e.g. adding artificial flow barrier, this method renders the whole history matching workflow applicable to practical problems. In summary, the proposed method can further enhance the quality of the model reconstructed from a training dictionary of geological models.

Challenged to remain competitive in a low oil price environment whilst navigating the energy transition, the industry is now seeking ways to evolve operations through digitalisation. Robotics has already enabled an evolution of the Oil and Gas industry by enabling deepwater operations through the deployment of ROVs. Now the industry seeks to reduce the unit operating costs of offshore facilities whilst improving safety and environmental performance. Autonomous underwater vehicles (AUVs) such as FlatFish can help the industry achieve this objective by reshaping the concept of operations for subsea surveillance. Oil and gas operators stand to benefit from robotic technologies which have been successfully adopted in other industries such as aerospace and defence. Technologies that can perform autonomous subsea inspections and interventions: faster, safer, and cheaper than traditional approaches. Robotics and AI (Artificial Inteligence) can play a fundamental role in increasing the competitiveness of the oil and gas industry, the prize for rapid deployment into daily subsea surveillance tasks means not only the unlocking of deep water ventures, but also the safe life extension of offshore assets. The FlatFish project is a leading example of how technology development in robotics and AI can contribute to the reduction of inspection costs, improve the accessibility of offshore installations and reduce safety risks. The technology development program has shown the capabilities of FlatFish AUV such as: performing autonomous visual inspection, tracking pipelines, hovering around structures such as tree systems and manifolds and returning to the docking station for recharging and data transfer. The development and pre-production qualification are in the final stage, next steps are the industrialization and commercialization. FlatFish is a leading example of where Robotics and AI can contribute towards a reduction in subsea inspection costs.

We explore refraction seismic for target-oriented reservoir characterization and monitoring, which is especially suited for the Brazilian pre-salt reservoirs. Long offset shots may be selected, such that refracted waves travel mostly horizontally over significant distances along the pre-salt reservoir before returning to surface to be recorded on ocean bottom receivers. The concept has been tested onshore, suggesting that an azimuthally complete set of source points may be enough for target-oriented FWI. However, for the pre-salt, we must overcome distortions from the overlying complex salt bodies. The data would improve static and dynamic reservoir modeling to manage production processes better. To accelerate the maturation of this concept, Shell conducted a field test using a circular geometry during the recent OBN survey over the Gato do Mato field. Here we discuss test planning, execution, and initial results. Presentation Date: Wednesday, October 14, 2020 Session Start Time: 9:20 AM Presentation Time: 10:10 AM Location: Poster Station 8 Presentation Type: Poster

Ultrasonic power and data transfer through multilayered curved walls was investigated using numerical and experimental analysis. The acoustic channel used in this paper was formed by two concentric pipes filled with water, aiming for applications that involve powering and monitoring sensors installed behind the pipe walls. The analysis was carried out in the frequency and time domains using numerical and experimental models. Power and data were effectively simultaneously transferred through the channel. A remote temperature and pressure sensor was powered and interrogated throughout all the layers, and the power insertion loss was 10.72 dB with a data transmission rate of 1200 bps using an amplitude modulated scheme with Manchester coding. The efficiency of the channel was evaluated through an experimental analysis of the bit error rate (BER) with different values of signal-to-noise ratio (SNR), showing a decrease in the number of errors compared with detection without Manchester coding.

A common problem in dolomite reservoirs is the heterogeneous distribution of porosity-reducing diagenetic phases. The intrasalt carbonates of the Ediacaran-Early Cambrian Ara Group in the South Oman Salt Basin represent a self-sourcing petroleum system. Depositional facies and carbonate/evaporite platform architecture are well understood, but original reservoir properties have been modified by diagenesis. Some of the carbonate reservoirs failed to produce hydrocarbons at acceptable rates, which triggered this study. The extent of primary porosity reduction by diagenetic phases was quantified using point counting. To visualize the distribution of diagenetic phases on a field scale, we constructed 2D interpolation diagenesis maps to identify patterns in cementation. The relative timing of diagenetic events was constrained based on thin-section observations and stable isotope analyses. Near-surface diagenesis is dominated by reflux-related processes, leading to porosity inversion in initial highly porous facies and a patchy distribution of early cements. This strong diagenetic overprint of primary and early diagenetic porosity by reflux-related cements leads to a reduction of stratigraphic and facies control on porosity. Calcite was identified as a burial-related cement phase that leads to an almost complete loss of intercrystalline porosity and permeability. Bitumen is an important pore-occluding phase and time marker of the deep-burial realm. The stratigraphic position of the dolomite reservoirs embedded at the base of a salt diapir had a strong impact on its diagenetic development. The salt isolated the dolomites from external fluids, leading to a closed system diagenesis and the buildup of near lithostatic fluid pressures. In combination, these processes decreased the impact of further burial diagenetic processes. The study highlights that cement distribution in salt-encased carbonate reservoirs is mainly related to early diagenetic processes but can be very heterogeneous on a field scale. Further work is needed to implement these heterogeneities in an integrated numerical reservoir model.

ABSTRACT We develop a workflow to estimate elastic attributes ( and ) of Pre‐Salt carbonate reservoirs using the Xu and Payne rock physics model in a depth‐variant multimineralogic fashion and apply it to one calibration and four extrapolation wells in a field in the Brazilian Santos Basin. Some of the parameters were stochastically simulated in a Bayesian framework to handle the model non‐uniqueness. Nuclear magnetic resonance porosity and mineralogic logs and x‐ray powder diffraction data from the studied wells and literature reference values were used for elastic solid media and dual‐porosity network modelling, bringing geological reasoning to the proposed workflow. Using rock physics templates, we improved our understanding of the reservoir petroelastic trends and devised alternative model parameterizations. Since the model was parameterized in a multimineralogic fashion with depth‐variant mineralogic information (e.g. volumes of carbonate, quartz and clay) as input, we improved elastic attribute estimations within the reservoir, increasing the model predictive power, compared to the common practice of allowing only porosity to vary with depth and representing the matrix properties by a depth‐invariant homogeneous or mixed mineral. We also investigated the workflow robustness and non‐uniqueness by studying its dependence on the choice of calibration well.

Abstract The Shell operated Parque das Conchas fields of the Brazilian deepwater block BC-10 utilize ESPs as the sole artificial lift method. Unlike traditional in-well deployed ESP systems, the ten BC-10 ESPs are deployed inside relatively shallow – 100m - caissons distributed across three subsea gathering areas, or Artificial Lift Manifolds at water depths from 1650 to 1900m. The integrated caisson, ESP and inflow/outflow valving is deployed and retrieved as a single unit and hence derives the name of a Pump Module, or Modulo de Bomba in Portuguese, or MOBO for short. As ESPs have much shorter run lives compared to the approximate 25 year field life, the BC-10 subsea operations asset team is responsible for managing and executing the rig based ESP replacement intervention campaigns, or Mobo interventions. As of 2015, nine ESP replacements have been executed in four campaigns. This paper examines the diverse aspects associated with the ongoing efforts to maintain the artificial lift system of BC-10 before, during and after a rig intervention campaign. Topics covered include the ESP failure analysis, equipment sparing philosophy, equipment and tooling preparations, offshore execution, vendor relationships and overall project management. Finally, learnings obtained during the four years of near continuous preparation and execution of MOBO interventions are discussed along with some planned future improvements.

Abstract The Parque das Conchas project is located in the BC-10 deepwater block in the Campos Basin, approximately 120 km southeast from the city of Vitoria, in water depths of 1,500 to 2,000 meters. Shell is the operator with a 50% equity share, with joint venture partners Petrobras (35%) and ONGC (15%)17. The wells flow via Caisson ESP’s to a host FPSO. Horizontal Open-hole Gravel Pack was the sand control method selected in the low frac margin reservoirs of the Ostra Field. In preparation for completing wells in formations with anticipated sand production, a large laboratory study was conducted evaluating the performance of stand-alone woven mesh screens and gravel packs. The study was performed to select a premium mesh screen which would minimize solids production in the event of incomplete gravel pack and manage fines production that would pass through the seabed located Caisson ESP systems. Additionally, it was important to control solids production to the Floating Production, Storage and Offloading (FPSO) unit as there were no provisions installed to handle large volumes of solids at the surface. The test grid compared the performance of multiple screen sizes with and without gravel packs with three unconsolidated formation particle size distributions which were representative of the size distributions anticipated in the completions. The resulting performance data correlated the amount of solids production, size of produced solids, retained gravel pack permeability and retained screen permeability as functions of the effective size of the formation material, the gravel size and the pore size of the screen. The gravel pack media size and premium screen mesh size were selected based on the results of this testing. Screen QA/QC was determined to be critical in the event a full gravel pack was not achieved and the screens would be expected to provide sand control in a stand-alone mode. An enhanced quality process was developed and implemented to ensure the highest quality of the screens was maintained and materials were traceable during well sand control completions. The laboratory tests identified combinations of screen and gravel packs which appeared to indicate the best choice for controlling solids production in an acceptable range while maintaining high flow capacity, maximizing production at minimum drawdown pressures. An enhanced QA/QC process for screen manufacturing contributed to the success of the completions. Production history for the first 2 years indicates minimal sand production.

Abstract Full‐waveform inversion is a wave equation–based imaging technique for obtaining subsurface model parameters by matching modelled with field data. Full‐waveform inversion is often formulated as a local optimization problem in which the model parameterization influences the gradient preconditioner and the convergence rate associated with the full‐waveform inversion objective function. Model parameterization governs the radiation pattern of the so‐called secondary Born source. In this work, we assess model parameterization effects on the estimation of P‐wave velocities using a three‐dimensional acoustic time‐domain full‐waveform inversion procedure. These include the three commonly used parameterization: velocity, slowness and squared slowness. In this context, we consider a field data set from a deepwater Brazilian pre‐salt field using a recently introduced circular shot ocean bottom node acquisition which favours refracted waves. The results reveal that the squared slowness model parameterization provides a satisfactory trade‐off between the reconstruction of the deep pre‐salt target area and convergence rate, saving 50% of runtime compared to the velocity and slowness cases.

We explore the potential of multi-scale full-waveform inversion based on q-statistics (q-FWI) for estimating velocities in typical Brazilian pre-salt fields. We work with simulated data in 2D generated in a realistic velocity model using conventional OBN acquisition and recently introduced circular shot OBN acquisition. In our implementation of FWI in the frequency domain, we explore three strategies to partition the range of frequencies to achieve an optimal inversion, and conclude that a partition into four sequential sets of frequencies provides the best cost/benefit solution. We find that the combination of conventional and circular shot OBN provides better results than each acquisition separately. Finally, by comparing with conventional FWI, we show that q-FWI reduces artifacts, widens the imaged area, and reduces computational cost. The lessons learned in 2D may be applied to 3D field data.

Summary Object-based static models are typically constructed for stratigraphically complex reservoirs. In this approach, the stratigraphic architecture is represented by use of distinct objects with specific geometric attributes and petrophysical characteristics. It is a major challenge to condition such models to production data while simultaneously maintaining the geologic realism and static conditioning. A novel work flow is developed for the assisted history matching (AHM) of object-based geomodels where the uncertain object locations and attributes are directly modified without resorting to (post-geomodeling) reparameterization techniques. It contains an object-modeling algorithm for channels and levees that provides direct access to the preraster geomodeling parameters for individual object locations and attributes. These parameters are gradually modified subject to physical constraints to achieve a history match. A fully integrated protocol is used in this process that automatically couples the static modeling algorithm with the reservoir simulator. The resulting work flow is moderated by a massively parallel and highly efficient iterative data-integration algorithm. In the AHM work flow, static and dynamic conditioning operations are respectively driven by separate objective functions and are performed at the iteration level in a sequential fashion. The static-conditioning step may add and remove objects in the geomodel, which changes the number of active AHM parameters over the course of the iterative search. The work flow handles such operations with minimal impact on the robustness of the search. A potential application of the direct AHM work flow for object-based geomodels is the identification of locations and attributes of channels in deepwater turbidite reservoirs, where the channels are typically below the resolution of the seismic data; the well spacing is typically larger than the characteristic object dimension, yet the production data exhibit strong sensitivity to channel connectivity. The concept of gradually adjusting the channel locations with the information in the production data (while maintaining static conditioning) is demonstrated on a real data set for a deepwater channelized-turbidite reservoir. The models proposed by the new AHM work flow not only improve the difficult-to-history-match injected-gas-breakthrough profiles but also provide geologically based explanations for them, taking into account the channel connectivity. The proposed AHM work flow ensures consistency across static and dynamic models by integrating multidisciplinary data with an easily auditable and replicable capability.