Zaluski, Wade (Schlumberger Canada LTD) | Andjelkovic, Dragan (Schlumberger Canada LTD) | Xu, Cindy (Schlumberger Canada LTD) | Rivero, Jose A. (Schlumberger Canada LTD) | Faskhoodi, Majid (Schlumberger Canada LTD) | Ali Lahmar, Hakima (Schlumberger Canada LTD) | Mukisa, Herman (Schlumberger Canada LTD) | Kadir, Hanatu (Schlumberger Canada Limited now with ExxonMobil) | Ibelegbu, Charles (Schlumberger Canada Limited) | Pearson, Warren (Pulse Oil Operating Corp) | Ameuri, Raouf (Schlumberger Canada Limited) | Sawchuk, William (Pulse Oil Operating Corp)
Enhanced oil recovery (EOR) is an economic way of producing the remaining oil out of previously produced Devonian Pinnacle Reefs in the Nisku Formation within the Bigoray area of Alberta. To maximize the recovery factor of the remaining oil, it was necessary to first characterize the geological structure, matrix reservoir properties, vugular porosity and the natural fracture network of these two carbonate reefs. This characterization model was then used for reservoir simulation history matching and production forecasting further discussed by (
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.
This course will present the workflows that have been developed along with spreadsheet-based exercises to solidify concepts. The workshop provides in-depth presentations and discussions of the models presented. This course examines datasets from both conventional and unconventional systems and present workflows to construct naturally-fractured reservoir models. Particular attention will be given to the use and calibration of a variety of 3D seismic attributes, which are critical to our characterization efforts. The combination of 3D seismic data with sound stratigraphic and structural frameworks provides a more robust fractured reservoir model.
The main goal for an operator developing an unconventional reservoir project is to maximize NPV per acre by optimizing its completion strategy. This can be achieved by applying a comprehensive approach that accounts for key well treatment controlling parameters, their impact on the future production performance, and economic uncertainty. In this work, we developed and applied a workflow to explore the impact of various completion parameters and determine the completion strategy with the maximum economic gain.
The workflow integrates petrophysical well log and core data, along with PVT lab experiments with normalized permeabilities calculated from microseismic attributes to initialize the reservoir model. The reservoir model is then calibrated using actual field data to generate a history matched model. Since this model is developed based on microseismic data and represents a realistic network of fractures created during stimulation, it can be further used to analyze the impact of main completion parameters, well spacing and configuration, on the production performance of the wells.
The workflow is applied to three wells drilled in a gas reservoir in the Marcellus Shale. Because abundant field data were available, we can be certain that the calibrated reservoir model accurately matches the reservoir behavior. Detailed analysis of the reservoir model shows the presence of undepleted zones which indicates the current well spacing is too wide. However, the frac hits recorded through microseismic monitoring and pressure interference with nearby wells suggests a tighter well spacing will result in energy loss and over-stimulation. Therefore, an economic analysis is used to evaluate the various well spacing and configuration scenarios and their implications in terms of cost-benefits.
Various well spacing scenarios are created for the original and the proposed chevron pattern well configurations. For each scenario, the EUR, NPV per well, and NPV per acre are calculated to represent maximum gas production, the overall profitability of the pad, and the economic success of the project, respectively. Three gas price scenarios are used for calculation of the NPV's to analyze the impact of the market condition on the economics of the project. The analysis demonstrates that tighter well spacing, independent of gas price, leads to the improved NPV per acre, reduction of EUR, and an increase in well communication as shown by the newly developed well communication index. The models reveal that a monotonic relation between well spacing and NPV per acre does not exist due to the complex nature of the created fracture network and competition between two opposite factors: frac hits that arises at tighter well spacing and unstimulated zones that diminish.
We showed that obtaining optimized well spacing and configuration could only be achieved through applying a comprehensive workflow that not only accounts for the impact of various well design and configuration parameters on production but also their economic implications defined in terms of NPV per acre. It is important to note that the integration of microseismic data was essential for the success of the workflow since it provides a realistic picture of the pathways connecting the adjacent wells which facilitate well communication.
Seismic data usually has lower vertical resolution than reservoir simulation models so it is a common practice to generate maps of 4D attributes to be used as the observed data to calibrate models. In such a case, simulation results are converted to seismic attributes and a map is generated by averaging the corresponding layers. Although this seems to be a fair practice, here we show that this procedure can present some drawbacks and propose a new approach to ensure a proper data comparison.
The first step of the proposed procedure follows the traditional sequence where seismic attributes are generated by running a petro-elastic model (PEM) with reservoir simulation data, at the simulation scale. Then, instead of averaging the simulation layers, we propose to resample the simulation grid to a seismic grid and filter the seismic impedances to the seismic frequency. Lastly, we extract the map from the regular grid to be compared with the observed 4D seismic. This procedure is performed in the depth domain and allows a straight and fair comparison of the two dataset.
A synthetic dataset based on a Brazilian field produced through water injection is used to validate this procedure. This dataset is composed by a synthetic 4D seismic data (observed data) generated by a consistent seismic modeling and inversion and a set of reservoir simulation models (to be matched). We computed seismic impedance for each simulation model by applying a PEM and two maps were generated for each model: (1) by averaging impedance values throughout the corresponding layers and (2) by applying the proposed procedure. When these maps are subtracted from the observed data (error maps), as would happen in a quantitative seismic history matching, we note a relevant differences. In the dataset used, we observed that if the vertical resolution issue is not considered (Case 1) the error map presents a strong bias that would erroneously force a decrease on the water saturation to match the observed data in a seismic history matching. While the map generated in Case 2 presents the errors better balanced and related to actual water movement differences rather than being a consequence of scale and resolution issues.
The novelty of this work is a quick way to bring simulation data to seismic resolution without going through all seismic modeling process ensuring a proper data comparison, which can be promptly added in seismic history matching process.
Until recently, reservoir characterization methods in the industry were limited to use of seismic technologies in exploration of oil and gas and had a very constrained role in production and development. In the past, using characterization for development fields was considered a very perilous task. Technological advancements and the risk-averse mindset have significantly expanded the application of reservoir characterization. Today, reservoir characterization is the basis of any development plans made for a commercial field.
Development of 3D reservoir modeling techniques to generate field development plans (FDPs) marked a step-change in reservoir characterization methods. Introduction of geostatistics and numerical simulation made it possible to build precise models to generate realistic field development scenarios. This is the state-of-the-art seismic-to-simulation method of reservoir characterization used in FDPs today. However, the struggle to estimate reservoir properties spatially away from the well continues.
Surface seismic data provide excellent areal coverage but do not provide the vertical resolution required for a fine-scale reservoir model. Geostatistical methods reduce the uncertainty in spatial distribution of petrophysical properties from pseudo-point supports (wells) but are not calibrated spatially between the wells. Correspondingly, the fluid saturation distribution and the parameters used in dynamically calculating the same during numerical simulation are not calibrated in the interwell space.
This paper details necessary data acquisitions and methods of calibration of 3D reservoir model to reduce uncertainty in the interwell space. The data acquisition methods have been available for some time, but have rarely been electronically incorporated in the 3D reservoir model and have been largely used to analytically guide the modeling and its inferences. A logical way of interpreting the results of acquisitions and calibrating the 3D reservoir model cell-by-cell is detailed in this paper.
Chen, Xin (BGP) | Zhang, Suhong (BGP) | Ou, Jin (CNODC) | Ye, Yufeng (CNODC) | Xu, Lei (CNODC) | Ma, Yingze (CNODC) | Wei, Xiaodong (BGP) | Yang, Ke (BGP) | Chen, Gang (BGP) | Zhou, Guofeng (BGP) | Xia, Yaliang (BGP) | Yan, Xiaohuan (BGP) | Zhang, Zeren (BGP) | Liu, Jingluan (BGP) | Zhou, Xiaoming (BGP)
In order to improve the accuracy of reservoir prediction results, the conventional method usually include seismic inversion, and seismic attribute analysis. Due to the limitation of the vertical resolution of seismic data, it is hard to identify the thin reservoir by seismic attributes directly. In order to improve the prediction accuracy of reservoir, this paper show a new reservoir characterization technique based on geological seismic conditioning. The new method mainly includes five steps. The first step is sedimentary facies classification based on the geological seismic analysis, such as core data, thin section analysis, FMI logging, NMR logging and conventional logging. The second step is modern sedimentary model optimization and forward modelling. In order to establish a reasonable sedimentary facies model, a similar barrier island modern sedimentary model was chosen. To understand the geological significance of seismic data, two different dominant frequency were designed for forward modelling based on the sedimentary facies model and petrophysical analysis. The third step is seismic conditioning under the guide of sedimentary facies model forward modelling. The next step is seismic constraint stochastic inversion, and the last step is reservoir characterization and new well confirm. The application of this method in A oilfield shows that the techniques not only improved the identification ability of the reprocessing seismic data, but also improved the prediction accuracy of the reservoir characterization results. This new reservoir characterization technique can integrated multidisplinary information, such as modern sedimentary model, well data and seismic data, to establish a reasonable sedimentary model, to enhance the resolution of seismic data by conditioning, and get an reasonable reservoir characterization results based on the seismic inversion.
Wang, GaoCheng (PetroChina Zhejiang Oilfield Company) | Zhao, Chunduan (Schlumberger) | Liang, Xing (PetroChina Zhejiang Oilfield Company) | Pan, Yuanwei (Schlumberger) | Li, Lin (PetroChina Zhejiang Oilfield Company) | Wang, Lizhi (Schlumberger) | Rui, Yun (PetroChina Zhejiang Oilfield Company) | Li, Qingshan (Schlumberger)
Huangjinba shale gas field is located at the south edge of the Sichuan Basin. It has very complex structures, in situ stresses and natural fracture corridors in comparison to adjacent areas in the Sichuan Basin. In recent drilling campaigns, drilling risks have caused some wells to fail in reaching their planned total depth, eventually failing to deliver cost-effective gas production. In order to mitigate drilling risks, e.g. mud loss, collapse, stuck, hang up, gas kick, effective drilling risk prediction is an urgent challenge to address. Integrating quantitative drilling risk prediction methods with qualitative methods could increase the prediction accuracy and avoid or mitigate the drilling risk during the well deployment stage.
In this project, multiple seismic attributes were used to predict natural fracture distributions which qualitatively indicated the locations where drilling risks were likely occur. Comprehensive geophysical characterization was performed to identify natural fracture zones and patterns, and their mechanisms were validated by analyzing regional geological and tectonic evolution.
Image log data was then integrated into the natural fracture distribution prediction from seismic to build a DFN (Discrete Fracture Network). This combination of the DFN predicted from seismic data plus quantitative image log information allowed improved accuracy in the prediction of drilling risks.
Following this, natural fracture stability was analyzed by building a 3D geomechanics model in order to predict drilling complex qualitatively. A full field 3D geomechanics model was built through integrating seismic, geological structure, log and core data. The 3D geomechanical model includes 3D anisotropic mechanical properties, 3D pore pressure, and the 3D in-situ stress field. Through leveraging measurements from an advanced sonic tool and core data, the anisotropy of the formation was captured at wellbores and propagated to 3D space guided by prestack seismic inversion data. 3D pore pressure prediction was conducted using seismic data and calibrated against pressure measurements, mud logging data, and flowback data. The discrete fracture network model, which represented multi-scale natural fracture systems, was integrated into the 3D geomechanical model during stress modeling to reflect the disturbance on the in-situ stress field by the presence of the natural fracture systems.
From these models, a drilling map which quantitatively indicated the depth where drilling risk such as mud loss, gas kick, etc. occurred was created along the well trajectory.
This paper presents the highlights and innovations in seismic multi-attributes analysis and full-field geomechanics modeling which integrate qualitative and quantitative methods for drilling risk prediction.
Amer, Aimen (Schlumberger) | Al-Wadi, Meshal (Kuwait Oil Company) | Salem, Hanan (Kuwait Oil Company) | Sajer, Abdulazziz (Kuwait Oil Company) | Al-Hajeri, Mubarak (Kuwait Oil Company) | Najem, Ali (Schlumberger)
Outcrop work represents the main source of analogs used to model subsurface reservoirs. Without such explanation of reservoir geometry, architecture, and characterization, producing subsurface formations would be largely uncertain. The aim of this paper is to build a geological static model for the Enjefa Beach outcrop exposed in Kuwait and use it to better understand subsurface reservoir architectures. This was achieved by acquiring several traverses along the outcrop, describing the various rock units, and understanding the depositional facies and facies associations. The next stage was to model each depositional unit as a separate zone embedded in an integrated model. This was followed by developing a forward synthetic three-dimensional seismic model to better understand how such reservoir architecture may appear in the subsurface. The final step was to use these findings in modeling a subsurface Cretaceous reservoir in northeastern Kuwait. The resultant model demonstrated that detailed geological complexities can be captured by conventional modeling techniques; in the model, the middle shoreface, upper shoreface, foreshore, and tidal channel complexes were statically modeled. Subsurface seismic data showed a series of highly sinuous meandering channels. Stacking patterns were found to vary among vertical, climbing, and compensational stacking patterns.
A. Alfataierge, J. L. Miskimins, T. L. Davis, and R. D. Benson, Colorado School of Mines Summary The 3D hydraulic-fracture-simulation modeling was integrated with 4D time-lapse seismic and microseismic data to evaluate the efficiency of hydraulic-fracture treatments within a 1 sq mile well-spacing test of Wattenberg Field, Colorado. Eleven wells were drilled, stimulated, and produced from the Niobrara and Codell unconventional reservoirs. Seismic monitoring through 4D time-lapse multicomponent seismic data was acquired by prehydraulic fracturing, post-hydraulic fracturing, and after 2 years of production. A hydraulic-fracture-simulation model using a 3D numerical simulator was generated and analyzed for hydraulic-fracturing efficiency and interwell fracture interference between the 11 wells. The 3D hydraulic-fracture simulation is validated using observations from microseismic and 4D multicomponent [compressional-wave (P-wave) and shear-wave (S-wave)] seismic interpretations. The validated 3D simulation results reveal that variations in reservoir properties (faults, rock-strength parameters, and in-situ stress conditions) influence and control hydraulic-fracturing geometry and stimulation efficiency. The integrated results are used to optimize the development of the Niobrara Formation within Wattenberg Field. The valuable insight obtained from the integration is used to optimize well spacing, increase reserves recovery, and improve production performance by highlighting intervals with bypassed potential within the Niobrara. The methods used within the case study can be applied to any unconventional reservoir. Introduction The Niobrara Formation is an organic-rich, self-sourcing unit composed of carbonate deposits in the form of alternating layers of chalks and marls. The Niobrara resource play is typically compared with the Eagle Ford Shale because of its high carbonate content. Early production can be dated back to 1976 from vertical wells in Wattenberg Field, although development was not deemed commercially viable at the time (Sonnenberg 2013). The shale play has become more attractive because of horizontal drilling and multistage hydraulic fracturing, allowing the Niobrara to be developed with overall success in the Denver-Julesburg Basin since 2009. The Niobrara Formation extends into several basins within the central USA involving Colorado, Wyoming, Nebraska, and Kansas.