When key geological scenario uncertainties, captured in multiple conceptual models, are combined with continuous parameters, the evaluation of a representative sample set quickly becomes unmanageable, laborious and too time consuming to execute. A workflow is presented that enables users to easily model conceptual as well as parametric uncertainties of the reservoir without the necessity of any complex scripting. The chain of models for all concepts is presented in one view, to provide overview of the key differences between concepts used. An ensemble of geologically sound samples can be created taking into account parameter dependencies and probabilities of concepts. The chain of models per concept can easily be (re)executed.
A case study is presented that consists of multiple concepts based on different hierarchical stratigraphic models in combination with different fault models, each of which with its own fluid- (defined contacts per compartment), grid- (sub-layering and areal resolution) and rock property models. Volumetric calculations are run on an ensemble to get static model observables like GRV, Pore Volume, Oil-In-Place, etc., reported by multiple sub-regions of the model in combination with a lease boundary. (When coupled with dynamic simulation, observables like ultimate recovery, break-through timing, etc. could also be obtained). As thousands of realizations were run concurrently, run time was reduced from weeks to hours. Results reveal the distribution and dependency of observables like GRV on top-structure-depth uncertainty and contact-level uncertainty. For in-place volumes the full suite of concepts and other parametric uncertainties including the stochastic uncertainties (i.e. seed) is analyzed. This also enables the identification of the key uncertainties that impact equity the most, which can be of great commercial value during equity negotiations. This workflow demonstrates how, with the power of Cloud computing, rigorous evaluation of multiple concepts combined with many parametric uncertainties has been achieved within practical turn-around times. As such it overcomes the prohibitive hurdles of the past that often have led to simplifications necessary to save time and effort. The result is better decision quality in resource development decisions.
Bigoni, Francesco (Eni S.p.A) | Pirrone, Marco (Eni S.p.A) | Trombin, Gianluca (Eni S.p.A) | Vinci, Fabio Francesco (Eni S.p.A) | Raimondi Cominesi, Nicola (ZFOD) | Guglielmelli, Andrea (ZFOD) | Ali Hassan, Al Attwi Maher (ZFOD) | Ibrahim Uatouf, Kubbah Salma (ZFOD) | Bazzana, Michele (Eni Iraq BV) | Viviani, Enea (Eni Iraq BV)
The Mishrif Formation is one of the important carbonate reservoirs in middle, southern Iraq and throughout the Middle East. In southern Iraq, the formation provides the reservoir in oilfields such as Rumaila/West Qurna, Tuba and Zubair. The top of the Mishrif Formation is marked by a regional unconformity: a long period of emersion in Turonian (ab. 4.4 My) regionally occurred boosted by a warm humid climate, associated to heavy rainfall. In Zubair Field, within the Upper interval of Mishrif Formation, there are numerous evidences of karst features responsible of important permeability enhancements in low porosity intervals that are critical for production optimization and reservoir management purposes.
In the first phase, the integration of Multi-rate Production logging and Well Test analysis was very useful to evaluate the permeability values and to highlight the enhanced permeability (largely higher than expected Matrix permeability) intervals related to karst features; Image log analysis, on the same wells, allowed to find out a relationship between karst features and vug densities, making possible to extend the karst features identification also in wells lacking of well test and Production logging information. This approach has allowed to obtain a Karst/No Karst Supervised dataset for about 60 wells.
In the second phase different seismic and geological attributes have been considered in order to investigate possible correlations with karst features. In fact there are some parameters that show somehow a correlation with Karst and/or NoKarst wells: the Spectral Decomposition (specially 10 and 40 Hz volumes), the detection of sink-holes at top Mishrif on the Continuity Cube and its related distance, the sub-seismic Lineaments (obtained from Curvature analysis and subordinately from Continuity), distance from Top Mishrif. In the light of these results, the most meaningful parameters have been used as input data for a Neural Net Process ("Supervised Neural Network") utilizing the Supervised dataset both as a Trained dataset (70%) and as a Verification dataset (30%). A probability 3D Volume of Karst features was finally obtained; the comparison with verification dataset points out an error range around 0.2 that is to say that the rate of success of the probability Volume is about 80%.
The final outcomes of the workflow are karst probability maps that are extremely useful to guide new wells location and trajectory. Actually, two proof of concept case histories have demonstrated the reliability of this approach. The newly drilled wells, with optimized paths according to these prediction-maps, have intercepted the desired karst intervals as per the subsequent image log interpretation, which results have been very valuable in the proper perforation strategy including low porous intervals but characterized by high vuggy density (Karst features). Based on these promising results the ongoing drilling campaign has been optimized accordingly.
Contreras Perez, David Rafael (OMV E&P GmbH - Abu Dhabi) | Al Zaabi, Ruqaya Abdulla (ADNOC Offshore - GUL) | Viratno, Bernato (OMV E&P GmbH - Abu Dhabi) | Sellar, Christopher (OMV E&P GmbH - Abu Dhabi) | Susanto, Maria Indriaty (OMV E&P GmbH - Abu Dhabi)
This paper summarizes an efficient workflow for building a reliable static model reference case by improving the accuracy of well placement in a hydrocarbon bearing structure. This is beneficial in optimising upcoming well target position and trajectory planning as well as during the dynamic history matching process. In a non-operated venture, the ability to generate an up-to-date static model that maintains pace with operations, provides valuable insight to advise the operator on the upcoming drilling plan and continuously supports the dynamic model for reserves booking, is highly sought after.
The systematic approach described in this paper is applied to a geo-model from a Middle East carbonate reservoir consisting of over 50 wells with good quality PSDM seismic data. The workflow presented begins with seismic mapping, utilizing volume-based modelling techniques, followed by structural element correction using borehole images (e.g. structural formation dip and true stratigraphic thickness estimate) and finally introduces alternative control points, which enable drilled wellbore trajectories to be structurally anchored, based on layer thicknesses and structural trends within the target reservoir.
Using this approach it is possible to generate a consistent structural model that honours geological markers, measured dip ranges and structural trends seen from seismic data and image logs. During the process one learns more about data quality (e.g. scale of data resolution and depth of investigation), associated with specific fields and carbonate reservoirs through the interaction between geological, geophysical and petrophysical disciplines and ensures their correct use. Data are used to improve the raw interpreted seismic horizons by calibrating mapped thickness distribution against the well tops. 2D visualizations are generated on a well-by-well basis, including map views, curtain sections (along each horizontal well), composite cross-sections and 3D visualizations to show inter-well relationships within different geological layers. As a result the well is placed in the correct structural position. Correct well placement, especially of highly deviated/horizontal wells, provides more accurate identification of reservoir sweet spots, leading to improved well target position and trajectory planning for upcoming wells, and a robust baseline to achieve production/well test history match during the dynamic modelling process.
3D model is a valuable tool in reservoir management, provided its representativeness of reservoir dynamics.Traditional History Match mainly focuses on reproducing reservoir behavior at well scale. A good match is not always representative of fluid movements in the reservoir. The proposed approach for 3D model validation combines and compares the results of integrated production analysis, in particular flow paths identification, with history matching by using streamlines technology. Streamlines speed up the comparison process especially in complex 3D models.
The workflow is based on a massive Production Data Analysis (PDA) where geological and dynamic data are integrated to identify preferential paths followed by the different fluid phases during the producing life of the field. The main result is the Fluid Path Conceptual Model (FPCM) where aquifer and injected water movements are clearly identified. Once the flooded areas are detected, streamlines are traced on the history matched model in order to easily compare the simulated connections with hard information from PDA. Actions to improve the model representativeness are suggested and integrated in an iterative tuning process.
This paper presents the results of the methodology applied on two complex fields with different injection strategies. FPCMs resulting from PDA provided a powerful boost to drive the history match and speed up the whole process. Priority was given in reproducing the identified preferential paths rather than to perfectly match well production data (which can be also affected by allocation uncertainties) by means of local unrealistic adjustments.
Streamlines were run on Intersect simulation, proving to be a fast and powerful tool for the visualization and understanding of fluid movements in the 3D Model. Since streamlines are used as visualization tool and are traced on a corner point geometry grid using fluxes provided by reservoir simulation, the reliability of the simulation output is preserved.
Once the model is representative of the real field behavior, it can be used as predictive tool in Reservoir Management to optimize the current injection strategy, promoting most efficient injectors.
This paper discusses the use of a novel data-driven method for automated facies classification and characterization of carbonate reservoirs. The approach makes an extensive use of wireline and while drilling electrical borehole image logs and provides a direct and fast recognition of the main geological features at multi-scale level, together with secondary porosity estimation. This embodies an unbiased and valuable key-driver for rock typing, dynamic behavior understanding and reservoir modeling purposes in these puzzling scenarios.
The implemented methodology takes advantage of a non-conventional approach to the analysis and interpretation of image logs, based upon image processing and automatic classification techniques applied in a structural and petrophysical framework. In particular, the Multi-Resolution Graph-based Clustering (MRGC) algorithm that is able to automatically shed light on the significant patterns hidden in a given image log dataset. This allows the system to perform an objective multi-well analysis within a time-efficient template. A further characterization of the facies can be established by means of the Watershed Transform (WT) approach, based on digital image segmentation processes and which is mainly aimed at quantitative porosity partition (primary and secondary).
The added value from this data-driven image log analysis is demonstrated through selected case studies coming from vertical and sub-horizontal wells in carbonate reservoirs characterized by high heterogeneity. First, the MRGC has been carried out in order to obtain an alternative log-facies classification with an inherent textural meaning. Next, the WT-based algorithm provided a robust quantification of the secondary porosity contribution to total porosity, in terms of connected vugs, isolated vugs, fractures and matrix contribution rates. Finally, image log-facies classification and quantitative porosity partition have been integrated with production logs and pressure transient analyses to reconcile the obtained carbonate rock types with the effective fluid flows and the associated dynamic behavior at well scale.
The presented novel methodology is deemed able to perform an automatic, objective and advanced interpretation of field-scale image log datasets, avoiding time-consuming conventional processes and inefficient standard analyses when the number of wells to be handled is large and/or in harsh circumstances. Moreover, secondary porosity can be proficiently identified, evaluated and also characterized from the dynamic standpoint, hence representing a valuable information for any 3D reservoir models.
This paper presents a diagnostic workflow to understand and implement rock and fluid modeling in a diagenetically heterogeneous and hydrodynamically pressured Middle East carbonate field. The workflow allows interactive field data integration, provides guidance for reservoir property distribution and fluid contact generation in order to improve reserves and forecasting estimation. The workflow is useful to a reservoir modeler in QA/QC role and in this case it proves particularly applicable in an organization with constrained resources during the farm-in process. The workflow runs on numerical methods within the static model to avoid database discrepancy during the diagnostic process. Using the core (CCAL, SCAL), log and pressure database, the geoscientist can assess subsurface modeling outputs from the simplest to more complex deterministic scenarios. The process aims to minimize the discrepancy between data input and model output while continuously honoring the data, maintaining realistic correlations (e.g. between static permeability and water saturation) and respecting inherent uncertainty.
Using a data-rich Middle East carbonate reservoir, the pre- and post-diagnostic comparison of 3D modeled reservoir properties to the input data are demonstrated. Diagnostic steps have helped to understand potential subsurface scenarios and thus minimize the discrepancy post exercise. The value of the workflow is its ability to pinpoint the key uncertainties in rock and fluid modeling from the field’s vast dataset in a shorter diagnostic time. The application of the workflow in this carbonate reservoir case study increases the importance of geological and property driven rock type classification and its 3D distribution in matching the water saturation profile. This proved particularly challenging in this case study due to the field’s compartmentalization - fluid contact scenario.
In this work we discuss the successful application of our previously developed automated scenario reduction approach applied to life-cycle optimization of a real field case. The inherent uncertainty present in the description of reservoir properties motivates the use of an ensemble of model scenarios to achieve an optimized robust reservoir development strategy. In order to accurately span the range of uncertainties it is imperative to build a relatively large ensemble of model scenarios. The size of the ensemble is directly proportional to the computational effort required in robust optimization. For high-dimensional, complex field case models this implies that a large ensemble of model scenarios which albeit accurately captures the inherent uncertainties would be computationally infeasible to be utilized for robust optimization. One of the ways to circumvent this problem is to work with a reduced subset of model scenarios. Methods based on heuristics and ad-hoc rules exist to select this reduced subset. However, in most of the cases, the optimal number of model realizations must be known upfront. Excessively small number of realizations may result in a subset that does not always capture the span of uncertainties present, leading to sub-optimal optimization results. This raises the question on how to effectively select a subset that contains an optimal number of realizations which both is able to capture the uncertainties present and allow for a computationally efficient robust optimization. To answer this question we have developed an automated framework to select the reduced ensemble which has been applied to an original ensemble of 300 equiprobable model scenarios of a real field case. The methodology relies on the fact that, ideally, the distance between the cumulative distribution functions (CDF) of the objective function (OF) of the full and reduced ensembles should be minimal. This allows the method to determine the smallest subset of realizations that both spans the range of uncertainties and provides an OF CDF that is representative of the full ensemble based on a statistical metric. In this real field case application we optimize the injection rates throughout the assets life-cycle with expected cumulative oil production as the OF. The newly developed framework selected a small subset of 17 model scenarios out of the original ensemble which was used for robust optimization. The optimal injection strategy achieved an average increase of 6% in cumulative oil production with a significant reduction, approximately 90%, in the computational effort. Validation of this optimal strategy over the original ensemble lead to very similar improvements in cumulative oil production, highlighting the reliability and accuracy of our framework.
Yonebayashi, Hideharu (INPEX CORPORATION) | Iwama, Hiroki (INPEX CORPORATION) | Takabayashi, Katsumo (INPEX CORPORATION) | Miyagawa, Yoshihiro (INPEX CORPORATION) | Watanabe, Takumi (INPEX CORPORATION)
CO2 injection is one of widely applied enhanced oil recovery (EOR) techniques, moreover, it is expected to contribute to the climate change from a viewpoint of storing CO2 in reservoir. However, CO2 is well known to accelerate precipitating asphaltenes which often deteriorate production. To understand in-situ asphaltene-depositions, unevenly distributed in composite carbonate core during a CO2 flood test under reservoir conditions, were investigated through numerical modelling study.
Tertiary mode CO2 core flood tests were performed. A core holder was vertically placed in an oven to maintain reservoir temperature and to avoid vertical segregation. A composite core consisting of four Ø1.5" × L2.75" plug cores, which had similar porosity range but slightly varied air permeabilities, was retrieved from a core holder after the flooding test. The remaining hydrocarbon was extracted by Dean-stark method, and heptane insoluble materials were extracted from each plug core via IP-143 method to observe distribution of asphaltene deposits. The variation of asphaltene mass in plug cores was investigated to explain its mechanism thermodynamically.
The core flood test was completed to achieve a certain additional oil recovery by 15 pore volume CO2 injection without any unfavorable differential pressure. The remaining asphaltene mass in each plug core revealed a trend in which more asphaltene collected from the inlet-side core. We assumed a scenario to explain the uneven asphaltene distribution by incorporating the vaporized-gas-drive and CO2 condensing mechanism. Namely, asphaltenes deposited immediately when pure CO2 contacted with oil. The contact between more pure CO2 and oil might be more frequently occurred in inlet-side core. To reproduce the scenario, a cubic-plus-association (CPA) model was generated to estimate asphaltene precipitating behavior as injected gas composition varied. In the first plug core, more pure CO2 gas was considered to contact with fresh reservoir oil compared with the downstream cores which might have less pure CO2 because of its condensation. The light-intermediate hydrocarbon gas vaporized by CO2 was also considered to emphasize the trend of more asphaltene deposits in upstream-side cores. The CPA model revealed consistent phenomenon supporting the scenario.
There are a vast number of reservoirs with drill cuttings and core images that have classification problems associated with them. This could be due to the images not being classified in the first place, or the images may be available but the interpretation reports could be missing. Another problem is that images from different wells could be interpreted by different wellsite geologists/sedimentologists and hence result in an inconsistent classification scheme. Finally, there could also be the problem of some images being incorrectly classified. Ergo it would be desirable to have an unbiased objective system that could overcome all of these issues. Step in convolutional neural networks. Advances during this decade in using convolutional neural networks for visual recognition of discriminately different objects means that now object recognition can be achieved to a significant extent. Once the network is trained on a representative set of lithological classes, then such a system just needs to be fed the raw drill cuttings or core images that it has not seen before and it will automatically assign a lithological class to each image and an associated probability of the image belonging to that class. In so doing, images below a certain probability threshold can be automatically flagged for further human investigation. The benefit of such a system would be to improve reservoir understanding by having all available images classified in a consistent manner hence keeping the characterization consistent as well. It would further help to reduce the time taken to get human expertise to complete the task, as well as the associated cost.
Al-Farisi, Omar (Khalifa University of Science and Technology) | Zhang, Hongtao (Khalifa University of Science and Technology) | Raza, Aikifa (Khalifa University of Science and Technology) | Ozzane, Djamel (ADNOC) | Sassi, Mohamed (Khalifa University of Science and Technology) | Zhang, TieJun (Khalifa University of Science and Technology)
Automated image processing algorithms can improve the quality and speed of classifying the morphology of heterogeneous carbonate rock. Several commercial products have worked to produce petrophysical properties from 2D images and with less extent from 3D images, relying on image processing and flow simulation. Images are mainly micro-computed tomography (μCT), optical images of thin-section, or magnetic resonance images (MRI). However, most of the successful work is from the homogeneous and clastic rocks. In this work, we have demonstrated a Machine Learning assisted Image Recognition (MLIR) approach to determine the porosity and lithology of heterogeneous carbonate rock by analyzing 3D images form μCT and MRI. Our research method consists of two parts: experimental and MLIR. Experimentally, we measured porosity of rock core plug with three different ways: (i) weight difference of dry and saturated rock, (ii) NMR T2 relaxation of saturated rock, and (iii) helium gas injection of rock after cleaning and drying.
We performed MLIR on 3D μCT and MRI images using random forest machine-learning algorithm. Petrophysicist provided a set of training data with classes (i.e., limestone, pyrite, and pore) as expert knowledge of μCT Image intensity correspondence to petrophysical properties. MLIR performed, alone, each task for identifying different lithology types and porosity. Determined volumes have been checked and confirmed with three different experimental datasets. The measured porosity, from three experiment-based approaches, is very close. Similarly, the MLR measured porosity produced excellent results comparatively with three experimental measurements, with an accuracy of 97.1% on the training set and 94.4% on blind test prediction.