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.
A flow simulation-driven time-lapse seismic feasibility study is performed for the Amberjack field that leverages existing multi-vintage 4D time-lapse seismic data. The focus is a field consisting of stacked shelf and deepwater reservoir sands situated in the Gulf of Mexico in Mississippi Canyon Block 109 in 1,030 ft of water. The solution leverages seismic interpretation, seismic inversion, earth modeling, and reservoir simulation [including embedded petro-elastic modeling (PEM) capabilities] to enable the reconciliation of data across multiple seismic vintages and forecast the optimal future seismic survey acquisition in a closed-loop. The overarching feasibility solution is integrated and simulation-driven involving multi-vintage seismic inversion, spatially constraining the petrophysical property model by seismic inversion, and performing reservoir simulation with the embedded PEM. The PEM is used to compute P-impedance and Vp/Vs dynamically, which enables tuning to both historical production and multi-vintage seismic data. The process considers a hybrid fine-scale 3D geocellular model in which the only upscaling of petrophysical properties occurs when the P-impedance from seismic inversion is blocked to the 3D geocellular grid. This process minimizes resampling errors and promotes direct tuning of the simulator response with registered seismic that has been blocked to a geocellular earth model grid. The results illustrate a three-part simulation-to-seismic calibration procedure that culminates with a prediction step which leads to a simulation-proposed time-lapse seismic acquisition timeline that is consistent with the calibrated reservoir simulation model. The first calibration tunes the model to historical production profiles. The second calibration reconciles the dynamic P-impedance estimate of the simulated shallow reservoir with that of the seismic inversion blocked to the 3D geocellular grid. The combination of these two steps outline a seismic-driven history matching process whereby the simulation model is not only consistent with production data but also the subsurface geologic and fluid saturation description. Large and short wavelength disparities in the P-impedance calibration existing between the simulator response and the time-lapse seismic data are attributed to resampling errors as a result of seismic inversion-derived P-impedance being blocked to the 3D geocelluar grid, as well as sparse well control in the earth model which leads to the obscuring of some asset-specific characteristics. The results of the third calibration step show how the time-lapse seismic feasibility solution accurately confirms prior seismic surveys undertaken in the asset. Given this confirmation, the solution achieves a suitable prediction of seismic-derived rock property response from the reservoir simulator as well as the optimal future time-lapse seismic acquisition time.
Identification of tidal channels fairways is key for predicting behavior of areas at higher risk to water breakthrough or otherwise have a significant impact on the development and monitoring of reservoir performance. However, tidal channels in carbonates are not often easily characterized using conventional seismic attributes. It is important to decipher the complexity of the carbonate tidal channel architecture with integrated multisource data and a variety of approaches.
In this paper, petrological characteristics and petrographic analysis is conducted on well logs and validated carefully using core data. Then, the second step is to compare the carbonate channel systems with modern analogue in Bahama tidal flat and outcrop scales in Wadi Mi'Aidin (Northern Oman). Thereafter, the supervised probabilistic neural network (PNN) and linear regression method were undertaken to detect an additional channel distribution.
The relationship of high porosity with low acoustic impedance appeared mostly in the channel facies which reflects good reservoir quality grainstone channels. Outside these channels, the rock is heavily mud filled by peritidal carbonates and characterized by a high acoustic impedance anomaly with low quality of porosity distribution. The new observation of PNN porosity volume revealed a lateral distribution of the Mishrif carbonate tidal channels in terms of paleocurrent direction and the connectivity. Additionally, the prior information from core data and the geological knowledge indicate a good consistency with classified lithology. These observations implied that Mishrif channels consist of a wide range of lithology and porotype fluctuations due to the impact of depositional environment.
The work enables us to provide a new insight into the distribution of channel bodies, and petrophysical properties with quantification of their influence on dynamic reservoir behavior of the main producing reservoir. This work will not only provide an important guidance to the development and production of this case study, however also deliver an integrated work path for the similar geological and sedimentary environment in the nearby oil fields of Southern Iraq.
In this paper, the approach to multivariate static and dynamic modeling is considered on the example of an offshore field discovered in 2017. Based on the limited volume of information, the quantitative and qualitative description of uncertainties included further in the 3D modeling is made. This model is proposed to be used as a tool for prompt decision making when implementing a fast-track project with limited time between exploration and pre-FEED stages.
Taha, Taha (Emerson Automation Solutions) | Ward, Paul (Emerson Automation Solutions) | Peacock, Gavin (Emerson Automation Solutions) | Heritage, John (Emerson Automation Solutions) | Bordas, Rafel (Emerson Automation Solutions) | Aslam, Usman (Emerson Automation Solutions) | Walsh, Steve (Emerson Automation Solutions) | Hammersley, Richard (Emerson Automation Solutions) | Gringarten, Emmanuel (Emerson Automation Solutions)
This paper presents a case study in 4D seismic history matching using an automated, ensemble-based workflow that tightly integrates the static and dynamic domains. Subsurface uncertainties, captured at every stage of the interpretative and modelling process, are used as inputs within a repeatable workflow. By adjusting these inputs, an ensemble of models is created, and their likelihoods constrained by observations within an iterative loop. The result is multiple realizations of calibrated models that are consistent with the underlying geology, the observed production data, the seismic signature of the reservoir and its fluids. It is effectively a digital twin of the reservoir with an improved predictive ability that provides a realistic assessment of uncertainty associated with production forecasts.
The example used in this study is a synthetic 3D model mimicking a real North Sea field. Data assimilation is conducted using an Ensemble Smoother with multiple data assimilations (ES-MDA). This paper has a significant focus on seismic data, with the corresponding result vector generated via a petro-elastic model. 4D seismic data proves to be a key additional source of measurement data with a unique volumetric distribution creating a coherent predictive model. This allows recovery of the underlying geological features and more accurately models the uncertainty in predicted production than was possible by matching production data alone.
A significant advantage of this approach is the ability to utilize simultaneously multiple types of measurement data including production, RFT, PLT and 4D seismic. Newly acquired observations can be rapidly accommodated which is often critical as the value of most interventions is reduced by delay.
Unexpected water accumulation (called perched water) can be present inside hydrocarbon bearing reservoirs. In case of limited or poor geophysical data, the prediction of this accumulation may be difficult.
In this paper, a real case is used to show how the presence of perched water was initially supposed and then verified through production data analysis.
During the development campaign of a deep water reservoir in West Africa, a water injector well found an unexpected shallower water table. To understand the nature of this water, the gas while drilling data of two oil producer drilled in the same area of the water injector were analysed. Based on this analysis the last meters of the open hole section of both oil producers were in water. The integration of gas while drilling data, stratigraphy, sedimentology and structural settings knowledge of the area suggested that this water was locally trapped during oil migration, most likely due to the presence of a structural barrier.
The two oil producer wells, located in the supposed perched water area, were successfully started-up. The behavior of both wells was daily monitored to understand and confirm the nature of perched water phenomenon. From day one, the two wells showed water production. After few weeks, the water cut of one well clearly started to reduce. For the other well, the water cut behavior was constant and only after one year of production the declining trend was appreciated. The observed declining trend of water production was the final confirmation that aquifer in this sector of the field is isolated and with limited extension. The water cut trend was also captured in the 3D dynamic reservoir model. In addition, tracers were implemented in the model to identify different water production sources (injected or perched) and to forecast their evolution during the field life.
The literature on perched water is quite limited and usually this kind of phenomenon is detected and described only on the geological side, but the production behavior of this water is rarely observed. This case study is integrating the geological and geophysical knowledge of the field with production data analysis to understand perched water behavior and can be considered a reference for other similar situation.
This study examines which is the margin of usability for Artificial Intelligence (AI) algorithms related to the rock properties distribution in static modeling. This novel method shows a forward modeling approach using neural networks and genetic algorithms to optimize correlation patterns among seismic traces of stack volumes and well rock properties. Once a set of nonlinear functions is optimized in the well locations, to correlate seismic traces and rock properties, spatial response is estimated using the seismic volume. This seismic characterization process is directly dependent on the error minimization during the structural seismic interpretation process, as well as, honoring the structural complexity while modeling. Previous points are key elements to obtain an adequate correlation between well data and seismic traces. The joint mechanism of neural networks and genetic algorithms globally optimize the nonlinear functions and its parameters to minimize the cost function. Estimated objective function correlates well rock properties with seismic stack data. This mechanism is applied to real data, within a high structural complexity and several wells. As an output, calibrated petrophysical time volumes in the interval of interest are obtained. Properties are used initially to generate a geological facies model. Subsequently, facies and seismic properties are used for the three-dimensional distribution of petrophysical properties such as: rock type, porosity, clay volume and permeability. Therefore, artificial intelligence algorithms can be widely exploited for uncertainty reduction within the rock property spatial estimation.
Pavlov, Dmitry (Sakhalin Energy Investment Company Ltd.) | Fedorov, Nikolay (Sakhalin Energy Investment Company Ltd.) | Timofeeva, Olga (Sakhalin Energy Investment Company Ltd.) | Vasiliev, Anton (Sakhalin Energy Investment Company Ltd.)
This paper summarizes the results of 3 years collaborative efforts of the Geophysicist, Production Geologist and Reservoir Engineers from the Astokh Development Team and a Geochemist from the LNG plant laboratory on integration of reservoir surveillance and reservoir modelling.
In period 2015 – 2018 a large bulk of geological and field development data was collected in Astokh field, in particular: cased and open hole logs, core, open hole pressure measurements, flowing and closed-in bottom hole pressures, well test data, new 4D seismic surveys (2015, 2018), fluid samples. Since 2016, essential progress was made in oil fingerprinting for oil production allocation in Astokh field. Simultaneously, the need for update of static and dynamic models was matured upon gaining experience in dynamic model history matching to field operational data (rates, pressures, well intervention results). In other words, the need in update of geological architecture of the Astokh reservoir model was matured upon reaching critical mass of new data and experience. To revise well correlation, it was decided to combine different sorts of data, in particular seismic, well logs and core data and reservoir pressures. Different pressure regimes were identified for 3 layers within XXI reservoir. Pressure transient surveys were used for identification of geological boundaries where it's possible and this data was also incorporated into the model. Oil fingerprinting data was used for identification of different layers and compartments. Integration of pressure and oil geochemistry data allowed to identify inter-reservoir cross-flows caused by pressure differential. Based on all collected data, sedimentology model and reservoir correlation were updated based on sequential stratigraphy. As a result, a new static model of main Astokh reservoirs was built, incorporating clinoform architecture for layers XXI-1' and XXI-2. To check a new concept of geological architecture material balance model was used and matched to field data
Integration of geological and field operational data provided a key to more advanced reservoir management and development strategy optimization. Based on updated reservoir model, new potential drilling targets were identified. Also, with new well correlation, water flood optimization via management of voidage replacement ratio was proposed. The completed work suggests essential improvement in reservoir modelling process by inclusion of various well and reservoir surveillance data.
The paper consists of the following sections: Introduction Field geology Field development history Scope of work complete and main results Proposed well correlation update for XXI-1' and XXI-2 layers Integration of well logs, pressure and fluid analysis data Connectivity between layers XXI-S, XXI-1' and XXI-2 Integration of pressure and oil fingerprinting data Connectivity within layers XXI-S, XXI-1' and XXI-2 Results of pressure interference tests Testing of new well correlation concept in material balance model Proposed reservoir correlation updated based on seismic data New geological concept New depositional model Integration of core data Changes in reservoir architecture Conclusion Main results and impact on field development
Field development history
Scope of work complete and main results
Proposed well correlation update for XXI-1' and XXI-2 layers
Integration of well logs, pressure and fluid analysis data
Connectivity between layers XXI-S, XXI-1' and XXI-2
Integration of pressure and oil fingerprinting data
Connectivity within layers XXI-S, XXI-1' and XXI-2
Results of pressure interference tests
Testing of new well correlation concept in material balance model
Proposed reservoir correlation updated based on seismic data
New geological concept
New depositional model
Integration of core data
Changes in reservoir architecture
Main results and impact on field development
The US Bureau of Ocean Energy Management (BOEM) has issued a final environmental impact statement (PEIS) for proposed geological and geophysical surveys of the Gulf of Mexico regarding possible oil and gas development. Because marine seismic surveys are critical in finding offshore oil and gas, The International Association of Oil and Gas Producers and the International Association of Geophysical Contractors have collaborated on a position paper that assesses the effect of such work on marine mammals.