Xu, Wei (CNOOC Research Institute Co., Ltd.) | Chen, Kaiyuan (Beijing Key Laboratory of Unconventional Natural Gas Geological Evalution and Development Engineeing, China University of Geosciences Beijing) | Fang, Lei (Beijing Key Laboratory of Unconventional Natural Gas Geological Evalution and Development Engineeing, China University of Geosciences Beijing) | Zhang, Yingchun (CNOOC Research Institute Co., Ltd.) | Jing, Zhiyi (CNOOC Research Institute Co., Ltd.) | Liu, Jun (CNOOC Research Institute Co., Ltd.) | Zou, Jingyun (CNOOC Research Institute Co., Ltd.)
The lacustrine delta sandbody deposited in the north of Albert Basin is unconsolidated due to the shallow burial depth, which leads to an ultra-high permeability (up to 20 D) with large variation and poor diagenesis. Log derived permeability differs greatly with DST results. Thus, permeability simulation is challenging in 3D geomodeling. A hierarchical geomodeling approach is presented to bridge the gap among the ultra-high permeability log, model and DST results. The ultimate permeability model successfully matched the logging data and DST results into the geological model.
Based on the study of sedimentary microfacies, the new method identifies different discrete rocktypes (DRT) according to the analyis of core, thin section and conventional and special core analysis (e.g., capillary pressure). In this procedure, pore throat radius, flow zone index (FZI) and other parameters are taken into account to identify the DRT. Then, hierarchical modeling approach is utilized in the geomodeling. Firstly, the sedimentary microfacies model is established within the stratigraphic framework. Secondly, the spatial distribution model of DRT is established under the control of sedimentary microfacies. Thirdly, the permeability distribution is simulated according to the different pore-permeability relation functions derived from each DRT. Finally, the permeability model is compared with the logging and testing results.
Winland equation was improved based on the capillary pressure (Pc) data of special core analysis. It is found that the highest correlation between pore throat radius and reservoir properties was reached when mercury injection was 35%. The corresponding formula of R35 is selected to calculate the radius of reservoir pore throat. Reservoirs are divided into four discrete rock types according to parameters such as pore throat radius and flow zone index. Each rock type has its respective lithology, thin section feature and pore-permeability relationship. The ultra-high permeability obtained by DST test reaches up to 20 D, which belongs to the first class (DRT1) quality reservoir. It is located in the center of the delta channel with high degree of sorting and roundness. DRT4 is mainly located in the bank of the channels. It has a much higher shale content and the permeability is generally less than 50 mD. Through three-dimensional geological model, sedimentary facies, rock types and pore-permeability model are coupled hierarchically. Different pore-permeability relationships are given to different DRTs. After reconstructing the permeability model, the simulation results are highly matched with the log and DST test results.
This hierarchical geomodeling approach can effectively solve the simulation problem in the ultra-high permeability reservoir. It realizes a quantitative characterization for the complex reservoir heterogeneity. The method presented can be applied to clastic reservoir. It also plays a significant positive role in carbonate reservoir characterization.
The 3D geological model is an important tool, which is used for decision-making process at each step of reservoir lifecycle. In turn the quality of the model as well as field operating efficiency is directly dependent on the geologist's expertise level and requires continuous improvement of professional skills. In the digital technology era the software functions continuously enhance leading to increased number of trainings in order to implement new algorithms in the working process. The aim of the paper is to share the best practices of geological and petrophysical modelling on-job trainings applied in Scientific Technology Centre.
There are several web-resources used in the company in order to store operational regulations, corporate educational trainings, original workflows, created plug-ins etc. By dint of special module in the working software the above mentioned data sources were implemented into the integrated knowledge platform. Now new software functions can be explored on-the-job and without any charges for external trainings. The module affords the development of interactive training cases for new functions study. The results of case accomplishment can be saved and available for competency assesment. Another key aspect of the module is the automatization of model quality control provided by application of guided workflows. Additionally, the solutions for some cases can be found during the crossfunctional co-operation among geologists, petrophysicists and petroleum engineers in online question platform, which is the part of the professional groups.
The unique integrated approach for increasing the quality of geological models through training and education, which is developed and successfully implemented in Scientific Technology Centre, will be beneficial not only for other companies, which plan to create and apply the equivalent employee training system, but also for oil and gas industry in general.
The Gulf of Mexico, and more precisely the Wilcox trend, has long been considered as challenging area for developing profitable hydrocarbon fields. In fact, the safe drilling of deep offshore wells needs to take into account the geological and geomechanical complexities, generated by the different sedimentological and tectonic events that accompanied the development of the Wilcox trend. In the case of Buckskin field, located in Keathley Canyon protraction (Figure 1), and in order to overcome those challenges, we developed a workflow that ranks all the parameters related to the geometry, the geology, the rock quality and the geomechanics characteristics of the reservoir. The core of the workflow is articulated around a probabilistic method that will assess the uncertainty of the productivity index, based on experimental design and Monte Carlo simulation. The proposed workflow allowed the optimization of the PI of the well thanks to a highly deviated reservoir section at a depth below 24,000', combined with an optimal fracking job.
Vorobev, Vladimir (Gazpromneft-GEO, LLC) | Safarov, Ildar (Gazpromneft-GEO, LLC) | Mostovoy, Pavel (Gazpromneft Science & Technology Centre, LLC) | Shakirzyanov, Lenar (Gazpromneft-GEO, LLC) | Fagereva, Veronika (Gazpromneft Science & Technology Centre, LLC)
Eastern Siberia is characterized by the extremely complex geological structure. The main factors include multiple faults, trappean and salt tectonics, the complex structure of the upper part of the section (0–1200 m) and its high-velocity characteristic (5000–6000 m/s), the high degree of rock transformation by secondary processes, low formation temperatures (10–30°C), the mixed fluid composition (gas, oil and water), and low net thicknesses (5–7 m) of productive layers. The fields of the region are among the most complex ones in the world according to the BP Company's statistics. New seismic and geologic model based on complex analyses of core, well logs, well tests, seismic and electromagnetic data allowed the Gazpromneft-GEO company to drill a series of successful wells.
Gazpromneft-GEO, LLC.holds three oil and gas exploration and production licenses within the Ignyalinsky, Vakunaisky and Tympuchikansky (Chona field) subsurface blocks (Russia, Eastern Siberia, Irkutsk Region and Republic of Sakha (Yakutia)). The area of the blocks is 6,855 sq.km, 3,050 sq.km of which are covered by the 3D seismic and high-density electric prospecting (
The work was carried out within the frames of scientific research and field works at the Gazpromneft-GEO, LLC. fields in Eastern Siberia. The high-density full-azimuth ground-based seismic using the UniQ technology was performed in Russia for the first time. The electric exploration with the near-field time-domain electromagnetic method was carried out along the same lines for the first time in Russia as well. This allowed to form the high-density cube of geoelectric properties. Model based on the wells (Facies model, Petrophysics model) and field geophysical data (3D seismic survey, 3D electric exploration, gravimetric survey, magnetic survey) complexation was made. The use of the approach allows to reduce the number of wells required for exploration of fields by 40%.
Geostatistical-based models provide a considerable improvement for predictive reliability of dynamic models and the following reservoir management decisions. This study focuses on geostatistical modeling the Paleocene Zelten Carbonate reservoir in the Meghil field. The field was discovered in 1959 and production operations began in 1961. Nineteen wells have been drilled to date. The structural framework consists of three slightly asymmetrical anticlinal structures trending NW-SE with steeper dip on the SW flanks. Each of the structures are separated by major normal faults. Seismic interpretation suggests that carbonate build-ups are most likely present on the three separate structures. Edge detection was used to clarify the structural geometries and the presence of additional minor faults. Pillar gridding technique was used to develop the structural framework including four major faults that are partially sealed based on analysis of the available DST and production test data. Stratigraphic analysis indicates a local presentation of dolomitic limestone in the northern portion of the main and the western structures caused considerable litho-facies variation that impacted the distribution of the petrophysical properties. Basic and advanced formation evaluation the net reservoir thickness of about 15 feet with an average porosity of 17% and average water saturation of 35%. Geostatistical-based applications that combine the spatial statistics (e.g. the semivariogram) and the available well and core data were used to populate the reservoir model with porosity, permeability, facies (lithology), net/gross, and water saturation. A conceptual facies model was also used to constrain the reservoir property distributions. Sequential Gaussian Simulation (SGS) was used to populate the model with porosity and water saturation and Sequential Indicator Simulation (SIS) was used to populate the facies model with permeability. The modeling parameters (e.g. semivariogram, correlation coefficients) were significantly constrained by the limited number of wells. Based on the limited number of wells available the semivariogram analysis resulted in a spherical semivariogram model with major axis range of 1435 meters for porosity and 1800 meters for water saturation. Minor axis ranges were about 50% of the major axis ranges. Given the limited well data, a significant effort was made to document the potential impact of the semivariogram parameters on the original hydrocarbon in place (OHIP) estimates and the lateral stratigraphic continuity of reservoir properties. The deterministic approach resulted in place volume estimates of 60 MMBBL and the stochastic approach provided an estimate of 45 MMBBL.
The spectral simulation approach is a recently developed geostatistical method of stochastic reservoir property simulation. The method has theoretical advantages over classical methods and the present work studies its practical effectiveness applied to synthetic data and real case of oil reservoirs. The study provides analysis of simulations results and describes strengths and limitations of the spectral method as well as application domain where it is most efficient.
The spectral method is based on Fourier analysis of well log data and consists of three major steps: decomposition of the well logs into Fourier series, simulation of Fourier coefficients in the interwell space and reconstruction of synthetic logs at every lateral point as sum of the Fourier series. The method was implemented in a software application and was used by authors for simulation of three-dimensional reservoir properties.
The effectiveness of the spectral method was studied based on simulations of continuous variables on a synthetic model. Simulations performed by the spectral method were compared to simulation results obtained on the same data by the more traditional method of sequential Gaussian simulation (SGS) provided by a commercial software. Analysis of the results from quantitative and qualitative points of view showed that the spectral method performed better at reproducing vertical non-stationarities observed in well data.
Practical applicability of the spectral method was demonstrated by simulation of porosity on a real oil field model, characterized by increase of porosity along depth with higher values of porosity closer to the bottom of the simulated layer. It was shown that the spectral method handled better this type of heterogeneity and reproduced distribution of porosity along the vertical axis closer to that of well data.
Tavassoli, Shayan (The University of Texas at Austin) | Krishnamurthy, Prasanna (The University of Texas at Austin) | Beckham, Emily (The University of Texas at Austin) | Meckel, Tip (The University of Texas at Austin) | Sepehrnoori, Kamy (The University of Texas at Austin)
Storage of large amounts of CO2 within deep underground aquifers has great potential for long-term mitigation of climate change. The U.S. Gulf Coast is an attractive target for CO2 storage because of the favorable formation properties for injection and containment of CO2. Deltaic formations are one of the primary targeted depositional environments in the Gulf Coast. This paper investigates CO2 storage in deltaic saline aquifers through a combination of geological modeling and flow simulation.
The geological model in our study is developed based on a laboratory-scale 3D flume experiment replicating the formation of a delta structure and populated with geologic properties according to Miocene Gulf of Mexico natural analogues. We used invasion percolation simulations to understand the gravity- driven flow and the relationship between architecture, stratigraphy, and fluid migration pathways. The results were used to develop an upscaled model for compositional simulation with the key features of the original geological model and to determine injection schemes that maximize the injection capacity and minimize the amount of mobile CO2 in the formation. In order to achieve this, we used compositional reservoir simulations to study the pressure-driven flow and phase behavior.
The results of invasion percolation simulations were used to identify the key stratigraphic units affecting CO2 migration. The realistic geometries and high resolution of the model facilitate the transfer of results from synthetic to subsurface data. The results allow for the analysis of deltaic depositional environments, important stratigraphic surfaces, and their impact on CO2 storage. The reservoir simulation model and phase behavior were validated against available field and lab data. The results of reservoir simulations were used to investigate the effects of main mechanisms, such as gas trapping and solubilization, on storage capacity. We compared our simulation results on the basis of invasion percolation (gravity driven) and reservoir simulation (pressure driven). The comparison is helpful to understand the strengths and weaknesses of each approach and determine best practices to evaluate CO2 migration within similar formations.
The unique and extremely well characterized deltaic model allows for unprecedented representation of the depositional aquifer architecture. This research combines geologic modeling, flow simulation, and application for CO2 storage. The integrated conclusions will constrain predictions of actual subsurface flow performance and CO2 storage capacity in deltaic systems, while identifying potential risks and primary stratigraphic migration pathways. This research gives insights on prediction of CO2 storage performance and characterization of prospective saline aquifers.
A statistical screening methodology is presented to address uncertainty related to main geological assumptions in green field modeling. The goals are the identification of the entire range of uncertainty on production, learning which are the most impacting geological uncertain inputs and understanding the relationships between geological scenarios and classes of dynamic behavior.
The paper presents the methodology and an example application to a green field case study. The method is applied on an ensemble of reservoir models created by combining geological parameters across their range of uncertainty. The ensemble of models is then simulated with a selected development strategy and dynamic responses are grouped in classes of outcome through clustering algorithms. Ensemble responses are visualized on a multidimensional stacking plot, as a function of the geological input, and the most influential parameters are identified by axes sorting on the plot. Geological scenarios are then classified on dynamic responses through classification tree algorithms. Finally, a representative set of models is selected from the geological scenarios.
The example study application shows a final oil recovery uncertainty range of 4:1, which is reasonable for a green field in lack of data. Such high range of uncertainty could hardly be found by common risk assessment based on fixed geological assumptions, which often tend to underestimate uncertainty on forecasts. Ensemble outcomes are grouped in four classes by oil recovery, plateau strength, produced water, and breakthrough time. The adoption of such clustering features gives a broad understanding of the reservoir dynamic response. The most influential geological inputs among the examined structural and sedimentological parameters in the example application result to be the fault orientation and channel fraction. This screening result highlights the main drivers of geological uncertainty and is useful for the following scenario classification phase. Classification of the geological scenarios leads to five classes of geological parameter sets, each linked to a main class of dynamic behavior, and finally to five representative models. These five models constitute an effective sampling of the geological uncertainty space which also captures the different types of dynamic response.
This paper will contribute to widen the engineering experience on the use of machine learning for risk analysis by presenting an application on a real field case study to explore the relationship between geological uncertainty and reservoir dynamic behavior.
Weijermans, Peter-Jan (Neptune Energy Netherlands B.V.) | Huibregtse, Paul (Tellures Consult) | Arts, Rob (Neptune Energy Netherlands B.V.) | Benedictus, Tjirk (Neptune Energy Netherlands B.V.) | De Jong, Mat (Neptune Energy Netherlands B.V.) | Hazebelt, Wouter (Neptune Energy Netherlands B.V.) | Vernain-Perriot, Veronique (Neptune Energy Netherlands B.V.) | Van der Most, Michiel (Neptune Energy Netherlands B.V.)
The E17a-A gas field, located offshore The Netherlands in the Southern North Sea, started production in 2009 from Upper Carboniferous sandstones, initially from three wells. Since early production history of the field, the p/z plot extrapolation has consistently shown an apparent Gas Initially In Place (GIIP) which was more than 50% higher than the volumetric GIIP mapped. The origin of the pressure support (e.g. aquifer support, much higher GIIP than mapped) and overall behavior of the field were poorly understood.
An integrated modeling study was carried out to better understand the dynamics of this complex field, evaluate infill potential and optimize recovery. An initial history matching attempt with a simulation model based on a legacy static model highlighted the limitations of existing interpretations in terms of in-place volumes and connectivity. The structural interpretation of the field was revisited and a novel facies modeling methodology was developed. 3D training images, constructed from reservoir analogue and outcrop data integrated with deterministic reservoir body mapping, allowed successful application of Multi Point Statistics techniques to generate plausible reservoir body geometry, dimensions and connectivity.
Following a series of static-dynamic iterations, a satisfying history match was achieved which matches observed reservoir pressure data, flowing wellhead pressure data, water influx trends in the wells and RFT pressure profiles of two more recent production wells. The new facies modeling methodology, using outcrop analogue data as deterministic input, and a revised seismic interpretation were key improvements to the static model. Apart from resolving the magnitude of GIIP and aquifer pressure support, the reservoir characterization and simulation study provided valuable insights into the overall dynamics of the field – e.g. crossflows between compartments, water encroachment patterns and vertical communication. Based on the model a promising infill target was identified at an up-dip location in the west of the field which looked favorable in terms of increasing production and optimizing recovery. At the time of writing, the new well has just been drilled. Preliminary logging results of the well will be briefly discussed and compared to pre-drill predictions based on the results of the integrated reservoir characterization and simulation study.
The new facies modeling methodology presented is in principle applicable to a number of Carboniferous gas fields in the Southern North Sea. Application of this method can lead to improved understanding and optimized recovery. In addition, this case study demonstrates how truly integrated reservoir characterization and simulation can lead to a revision of an existing view of a field, improve understanding and unlock hidden potential.
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.