Dhote, Prashant (Kuwait Oil Company) | Al-Adwani, Talal (Kuwait Oil Company) | Al-Bahar, Mohammad (Kuwait Oil Company) | Al-Otaibi, Ahmad (Kuwait Oil Company) | Chakraborty, Subrata (Schlumberger) | Stojic, Slobodan (Schlumberger)
Subsurface petroleum industry is burdened with uncertainties in every aspect from exploration to production due to limitations of accessibility to reservoir and technology. The most important tools used to understand, quantify and mitigate the uncertainties are geostatistical static modeling and numerical dynamic simulation geomodels. Geomodels are widely used in the industry for characterizing the reservoir and planning favorable development strategy. It is vital instrument for maximizing asset value and optimize project economics.
Static geomodels are foundation for all the advanced numerical and analytical solutions to solve the intricacies of reservoir performance. At the same time, it is where all the static and dynamic geological and engineering observations get integrated to develop common understanding of the reservoir for future studies. Understanding of the above observations and imaging of reservoir framework by individual is the basis for building static geomodels. Hence, at time, the process is highly subjective and proper QC'ing of the models to achieve the general and specific modeling objectives becomes imperative. Simple Questionaries’ based QC'ing and ranking methodologies are also controlled by subjectivity and individual preferences.
In the present endeavor, quantitative ‘Key Performance Indicators (KPIs)’ based standard static geomodeling practices and QC'ing methodologies at corporate level are developed in specially designed "Process Implementation Project (PIP) – Hydrocarbon resource and Uncertainty Management"’ under the aegis of ‘Kuwait Oil Company (KOC) - Reservoir Management Best Practices Steering Committee'.
The main objectives are to establish a practical modeling process, workflows and criteria to standardize modeling processes. A structured self-guidling modeling document has been developed with self-assemment guidelines and questionary. Finally, for each individual process a set of KPIs are specified as minimum standard to meet to obtain the approval of static model.
The present efforts are important for any geologists, geomodelers and reservoir engineers dealing with geostatistical and numerical reservoir modeling and will provide the KPI's based general practices for quality assurance (QA) and QC'ing of the models.
Reservoir modeling and the derived fluid production over time curves are a key part of the workflows associated with major capital project decisions. These models may be very complex and use a variety of geological constraints in an effort to develop the porosity, permeability, and saturation distributions used in dynamic models (with or without upscaling). Over time and partially in response to increased computing capability as well as the need for more realistically heterogeneous models, model size as measured by number of model cells and model complexity has increased but model-derived production forecasts remain optimistic. This paper, one of a series that now stretches back over a decade, addresses a number of modeling issues with the goal of (1) better understanding how modeling workflows may contribute to forecast optimism and (2) what reservoir modelers, both geologists and engineers, may do to reduce forecast optimism derived from their subsurface models by improved understanding of how model parameters such as grid size, number of grid cells, semivariogram parameters (e.g. the range), and number of geological/stratigraphic "control" surfaces used to constrain models. Adequate modeling of reservoir heterogeneity appears to require very to extremely large models (e.g. large number of small cells). Many of the parameters used to "control" heterogeneity including the semivariogram range parameter, the number of "detailed" stratigraphic layers, and the number of rock/facies "containers" or model regions appears to have only a small impact on forecast recovery.
Nandurdikar and Wallace (2011) industry-wide lookback summary showed that recovery forecasts made prior to project sanction averaged about 25-50% higher than actual observed reservoir performance. This study has examined the potential quantitative impact of sparse data and "decision" bias on reservoir recovery forecasts using a synthetic data set. The approaches utilized included statistical analysis of forecasts derived from a synthetic analog data set as the number of analogs was reduced from the full set of reservoirs to as few as 25% of the available reservoirs using random as well as biased selection criteria. The latter bias experiments allowed some inference to be made as to the potentially large impact of management "decision" bias. Statistical results obtained from the synthetic and actual case histories suggest that sparse data "impact" may account for up to 10-25% of the observed optimism and that decision bias for up to 15-25% of the observed optimism. Although not investigated in detail in this study, prior work (Meddaugh, 2015) suggested that other sources of potential bias such as stochastic geological model parameters (e.g. the semivariogram model) and upscaling are generally small with the possible exception of static/dynamic model grid parameters (particularly dynamic model cell size) and well location optimization workflows. Improved quantitative understanding of the potential impact of sparse data and decision bias on reservoir forecasts will enable the industry to reduce the forecast optimism noted by Nandurdikar and Wallace (2011) and will likely improve capital efficiency in the industry.
The spatial stacking structure and contact relationship between point bars in meandering river reservoir affect the reservoir continuity, interwell injection-production connectivity and distribution of remaining oil. But its characterization and modeling is hard to achieve in traditional modeling algorithm or deterministic microfacies drawing method, especially in offshore oil fields with large well spacing and low resolution seismic data. In order to solve this problem, we developed a new prediction and modeling method based on meander loop database and automatic reconstruction algorithm of sedimentary pattern. This algorithm honors the geometries and interrelationships between point bars while honoring the conditioning data. The general methodology consists of 2 steps. (1) Build a database storing the geometries of meander loops. (2) Select the meander loop from database sequentially and place it in the simulation space constrained by predefined rule and conditioning property.
We applied this method to construct the stacked point bar model of M oilfield. Multiple equal probable architecture models were built and were compared with each other through history matching. The optimal predicted model reaches a history matching coincidence rate of 91.3% and was used to predict the abundance zones of remaining oil. Based on the prediction results, 13 adjustment horizontal wells were drilled in the study area to tap the remaining oil. The accurate predicted result implies that the pattern reproduction based on database are effective for characterizing the spatial structure of stacked point bar reservoir.
Ma, Eddie (Kuwait Oil Company) | Gheorghiu, Sorin (Schlumberger) | Banagale, Merlon (Kuwait Oil Company) | Dashti, Laila (Kuwait Oil Company) | Bond, Deryck (Kuwait Oil Company) | Ibrahim, Muhammad (Schlumberger) | Ali, Farida (Kuwait Oil Company) | Gurpinar, Omer (Schlumberger)
The Greater Burgan field in Kuwait is the largest clastic oil field in the world. Its sheer size, complex geology, intricate surface facility network, 5, 000 well-completions and 68-years of production history represent formidable challenges in reservoir simulation. In the last two decades, many flow simulation models, part-field and full-field, were developed as reservoir management tools to study depletion plan strategies and reservoir recovery. The new 2013 Burgan flow simulation was a major undertaking in terms of effort and financial cost. The model size, innovative technology, supporting resources, integrated workflow and meticulous planning applied to this project were unprecedented.
As the Burgan field has matured over time, the reservoir pressure has declined in certain areas, with associated reduced productivity. The reduction of wells' productivity, combined with the increasing water production, has necessitated improved oil recovery (IOR) initiatives in order to meet the Kuwait Oil Company (KOC) corporate vision-2030, sustaining oil production and ensuring high recovery from Burgan reservoirs. This paper describes the development of a dynamic model to design pressure maintenance projects for optimal reservoir management and IOR strategies. It was built on a history match model which has a 68-years of history matching on three levels, Global (Field), Regional (Reservoirs / Gathering Centers) and wells. These three levels depict the concerted history matching effort in accordance with the recurrent data quality. Details of geologic and dynamic modeling have been documented and presented in previous Burgan SPE papers and are not repeated in this paper.
The primary objectives of the Burgan prediction model are meeting the production target profiles with optimal field development plans (FDP) and to maximize oil recovery. There are two pressure maintenance projects, Wara Pressure Maintenance Project (WPMP) and Burgan Sand Upper (BGSU-PMP), included in the prediction model. In this paper, WPMP is discussed in detail as the waterflood project is now entering operation stage after 10 years of planning and construction. BGSU-PMP is part of the Burgan FDP but is not focused within the scope of this paper.
Sub-surface modeling in the giant Greater Burgan field complex is not just enormous, it is also arduous and challenging. The accomplishment by the team was momentous despite a less-than-expected result. Nonetheless, lessons learnt offered valuable information for future improvement. It has been a long and difficult journey from geological model to dynamic model over the last five years. Yet, in pursuing IOR and EOR, the journey has just begun.
Glenton, P.N. (Esso Australia Pty Ltd) | Sutton, J.T. (Esso Australia Pty Ltd) | McPherson, J.G. (ExxonMobil Exploration Co.) | Fittall, M.E. (Esso Australia Pty Ltd) | Moore, M.A. (Esso Australia Pty Ltd) | Heavysege, R.G. (ExxonMobil Exploration and Production Malaysia, Inc.) | Box, D. (ExxonMobil Upstream Research Co.)
The Scarborough gas field was discovered by Scarborough-1 in 1979 in the Carnarvon Basin on the Australian North West Shelf. The field is 285 km offshore in water depths of 900 to 1000 m, and contains about 16 Tcf OGIP of very dry gas within a large, very low-relief faulted anticline covering about 800 sq. km. It has been appraised by four additional wells and a 3D seismic survey, and is being evaluated for development.
The Scarborough reservoir consists of Early Cretaceous deepwater turbidite sands deposited in a basin-floor fan setting. These sands were sourced from the expansive, northward-prograding Barrow Group fluvio-deltaic system located some 50 km to the south of Scarborough. The reservoir interval is a three-tiered fan sequence with variable sand content and quality: a high-quality, high net-to-gross Lower Fan unit which contains the majority of the gas-in-place, overlain by lower net-to-gross and lower quality Middle and Upper fans. The dominant reservoir lithofacies are quartzose medium- and fine-grained sandstones which are largely unlithified and uncemented, with average porosities of greater than 30% and permeabilities of 100's to 1000's of millidarcies. The background lithofacies are mudstones and siltstones which straddle the silt-mud boundary.
Static geological models and dynamic flow simulation models have been used to integrate seismic and well data, petrophysical analysis, and sedimentologic and stratigraphic interpretation. Outcrop analogues for deepwater basin-floor fan systems include the extensive, well-exposed and well-studied Permian Ecca Formation of the southern Karoo Basin, South Africa, the Carboniferous Ross Formation of western Ireland, and the Eocene Ainsa Formation of northern Spain. These outcrop analogues suggest that siltstone facies are bottom-loaded within genetically related depositional packages.
Reservoir models were used to investigate the effects of stratigraphic organisation and lithofacies distribution on reservoir performance predictions. By reference to outcrop analogues, an hierarchical approach was developed to systematically distribute depositional facies and lithofacies within the models. This permitted investigation of the effect of stratigraphic features of different scales on predicted production rates, reservoir performance and individual well performance.
Three hierarchical levels of facies models were incorporated into the stratigraphic zones. Two levels define deterministic and stochastic depositional facies geometries, and the third and finest level is lithofacies, or reservoir rock type. Seismic data were used to map large-scale depositional facies elements, and lithofacies were interpreted from well logs calibrated to conventional cores.
The use of lithofacies distributed within depositional facies provides flexibility in the modelling workflow, provides the template for distribution of the rock properties of porosity, horizontal and vertical permeability, and water saturation, and allows systematic investigation of the effect of siltstone baffles on predicted flow streams, particularly on the timing of water arrival.
Nurhono, Achmad Aprayoga (Petronas) | Kantaatmadja, Budi Priyatno (Petronas) | Masoudi, Rahim (Petronas) | Thu, Goh (Petronas) | Rahman, Nasir (Petronas) | Othman, Mohamad (Petronas Carigali Sdn Bhd) | Hernandez, Nina M. | Rahman, M. Ramziemran
The sedimentation in deepwater environments commonly includes deposition of thinly-bedded pay zones that are difficult to be characterized using standard seismic and logging techniques. Furthermore, these zones are often left unexploited and even overlooked during drilling, as they are finer in resolution than it can be detectable in conventional open-hole logs.
The paper presents an integrated multi-disciplinary study on thinly-bedded reservoir characterization in deep water areas in Malaysia. The adapted workflow consist of: (1) Seismic Data Conditioning, (2) Petrophysical SHARP Analysis, (3) Simultaneous and Rock Model Building, (4) Lithology Prediction, Hydrocarbon Volume, and Net pay, (5) Stochastic Seismic Inversion and Geo-statistical Modeling, and (6) Reservoir Simulation and Validation, (7) Uncertainty Analysis, (8) Sedimentological Analysis using Core-Image, and (9) Geomechanical Rock Property Analysis.
Petrophysical diagnostics using high quality resistivity images of OBMIs, as log input for thinly-bedded modeling, was the primary driver to establish effective elastic properties through AI vs. VP/VS cross plot (for lithology prediction) and AI vs. total porosity cross plot (for porosity prediction) within the model. These cross-plot transforms are then upscaled and applied to build a cascading of deterministic inversion (simultaneous AVO inversion) and stochastic inversion of 1-ms sampling, which are calibrated to core and neural network litho-facies interpretation for lithology and porosity modeling.
The geo-statistical modeling workflow was initially built-in with 7 exploration wells that have OBMIs (Oil Base Micro Imager) as the typical model. Numbers of reservoir properties realizations were generated by generating geo-cellular grid over the zone of interest. These realizations could provide an improved lithology, porosity and fluid determinations and could lead to estimate a more robust volumetric, particularly within such thinly-bedded reservoir. The developed unique integrated workflow was applied on the field under study showing about 30% increase in in-place volume and was successfully validated against available production/well data as well as new drilled wells.