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 (
Oil production from shale and tight formations will increase to more than 6 million barrels per day (b/d) in the coming decade, making up most of total U.S. oil production (> 50%). However, achieving an accurate formation evaluation of shale faces many complex challenges. One of the complexities is the accurate estimation of shale properties from well logs, which is initially designed for conventional reservoirs. When we use the well logs to obtain shale properties, they often cause some deviations. Therefore, in this work, we combine cores and well logs together to provide a more accurate guideline for estimation of total organic carbon, which is primarily of interest to petroleum geochemists and geologists.
Our work is based on Archie's equation. Resistivity log will lead to some incorrect results, such as total resistivity, when we follow the conventional interpretation procedure in well logs. Porosity is another complex parameter, which cannot be determined only by well log, i.e. density, NMR, and Neutron log. Therefore, the flowchart of TOC calculation includes five main parts: (I) the shale content calculation using Gamma log; (II) the determination of shale distributions using Density and Neutron logs and cross-plot; (III) the calculation of total resistivity at different distribution types; (IV) obtaining porosity using core analysis, NMR and density logs; and (V) the calculation of TOC from modified Archie's equation.
The results indicate that the shale content has a strong effect on estimation of water saturation and hydrocarbon saturation. Especially, the effect of shale content is exacerbated at a low water saturation. A more accurate flowchart for TOC calculation is established. Based on Archie's equation, we modify total resistivity and porosity by combining Gamma Log, Density Log, Neutron Log, NMR Log, and Cross-plot. An easier way to estimate porosity is provided. We combine the matrix density and kerogen density together and obtain them from core analysis. Poupon's et al. (1954) laminar model has some limitations when applying in shale reservoirs, especially at a low porosity.
Literature surveys show few studies on the flowchart of TOC calculation in shale reservoirs. This paper provides some insights into challenges of well logs, core analysis in shale reservoirs and a more accurate guideline of TOC calculation in shale reservoirs.
Rate-transient analyses (RTA) is a useful reservoir/hydraulic fracture characterization method that can be applied to multi-fractured horizontal wells (MFHWs) producing from low permeability (tight) and shale reservoirs. In this paper, a recently-developed three-phase RTA technique is applied to the analysis of production data from a MFHW completed in a low-permeability volatile oil reservoir in the Western Canadian Sedimentary Basin.
This new RTA technique is used to analyze the transient linear flow regime for wells operated under constant flowing bottomhole pressure conditions. With the new method, the slope of the square-root-of-time plot applied to any of the producing phases can be used to directly calculate the linear flow parameter,
The subject well, a MFHW completed in 15 stages, produces oil, water and gas at a nearly constant (measured downhole) flowing bottomhole pressure. This well is completed in a low-permeability, near-critical volatile oil system. For this field case, application of the new RTA method leads to an estimate of
The new three-phase RTA technique developed herein is a simple-yet-rigorous and accurate alternative to numerical model history-matching for estimating
Costin, Simona (Imperial Oil) | Smith, Richard (Imperial Oil) | Yuan, Yanguang (Bitcan Geoscience and Engineering) | Andjelkovic, Dragan (Schlumberger Canada) | Garcia Rosas, Gabriel (Schlumberger Canada)
Open-hole mini-frac tests are seldom performed in the Athabasca and Cold Lake oil sands due to the complexity of operations. In this paper we present the results of open-hole injections tests performed in Cold Lake, Alberta (AB), Canada. The objective of the injection tests was to assess the in-situ stress condition in the Cretaceous Colorado Group. The injection tests results combined with the run of formation image logs (FMI) before and after the injection have enabled not only the determination of the in-situ minimum stress in the rock, but also the full 3-D stress tensor, along with the orientation and inclination of the hydraulic fracture. The tests were performed in IOL 102/08-02-066-03W4 (N10 Passive Seimic Well, 'PSW'). The injection tests have revealed that the vertical stress in the area is the in-situ minimum stress, consistent with previous measurements. The hydraulically-induced fracture has sub-horizontal to moderate dip angle, mostly owing to the preexisting fabric of the rock, and peaks in the general NE-SW direction. Numerical modeling of the in-situ stresses has shown that the values of the vertical and the minimum horizontal stresses are close, with the vertical stress consistently being smaller than the minimum horizontal stress in all tested zones.
This paper outlines methods to characterize hydraulic fracture geometry and optimize full-scale treatments using knowledge gained from Diagnostic Fracture Injection Tests (DFITs) in settings where fracturing pressures are high.
Hydraulic fractures, whether created during a DFIT or larger scale treatment, are usually represented by vertical plane fracture models. These models work well in a relatively normal stress regime with homogeneous rock fabric where fracturing pressure is less than the Overburden (OB) pressure. However, many hydraulic fracture treatments are pumped above the OB pressure, which may be caused by near well friction or tortuosity but, may also result in more complex fractures in multiple planes.
Procedures are proposed for picking Farfield Fracture Extension Pressure (FFEP) in place of conventional ISIP estimates while distinguishing between storage, friction and tortuosity vs. fracture geometry indicators.
Analysis of FFEP and ETFRs identified in the DFIT PTA analysis method combined with the context of rock fabric and stress setting are useful for designing full-scale fracturing operations. A DFIT may help identify potentially problematic multi-plane fractures, predict high fracturing pressures or screen-outs. Fluid and completion system designs, well placement and orientation may be adjusted to mitigate some of these effects using the intelligence gained from the DFIT early warning system.
Optimizing steam-assisted gravity drainage (SAGD) performance in oil sands reservoirs relies on the quality of steam allocation decisions made across the well inventory. With finite facility steam generation capacity, SAGD producers are typically challenged with identifying the true opportunity cost of allocating steam volumes based on well performance. This paper presents a novel technique to inform steam allocation decisions and managing SAGD reservoir pressures in service of optimizing production and consequently improving the economic performance of the asset through smarter SAGD field development planning.
The concept of marginal steam-oil-ratio (mSOR) is introduced as a method of guiding steam allocation decisions. Marginal SOR is defined as the cold-water equivalent volume of steam required to produce the next marginal barrel of bitumen from the production system in a steam constrained environment. The metric represents the opportunity cost of deploying a barrel of steam to the next best alternative in steam allocation decisions. Dynamic quantification of mSOR over the plausible range of operating pressures for each producing entity (PRDE) in the inventory (such as a well group or drainage area) is critical to optimally allocating steam when faced with reservoir challenges such as reservoir complexity and heterogeneity and transient reservoir behaviors such as thief zone interaction.
This paper prescribes methodologies to analytically and empirically quantify mSOR for a SAGD production system. Additionally, application of the concept if field production optimization is discussed under the context of integrated production modeling and constrained flow network optimization problems. A case example of applying mSOR to guide steam allocation decisions at ConocoPhillips' Surmont SAGD asset is presented under a steam constrained environment. The mSOR guided solution is validated using brute-force enumeration of steam allocation outcomes in the production system to prove production optimality. The results from this dynamic steam allocation strategy guided by mSOR characterization show significant improvements in field oil rates, field steam management efficiency and consequently the economic value of the SAGD asset.
Monitoring data of episodic transient heat and flow conditions, caused by intermittent cold CO2 injection in Aquistore, has shown a linkage between injectivity index and downhole injection temperature. Taking leverage access to invaluable field performance data collected from this highly instrumented Canadian CCS demonstration project, the focus of this paper is to understand and quantify the potential non-isothermal mechanisms involved in cold CO2 injection. Understanding this phenomenon is important as it has serious implications on containment, conformance, and injectivity technologies for effective geological CO2 storage.
To account for transient heat and fluid transport during cold CO2 injection in Aquistore, a non-isothermal EOS-based fluid flow simulation, of a high-resolution detailed geological model built based on an extensive characterization program, was calibrated with periodic monitoring data of downhole pressure, temperature, and injected mass rate. Due to the possibility of non-isothermal effects on near-wellbore stress fields, local induced fractures, and permeability alterations, in addition to dynamics of CO2-brine interactions, coupled reservoir geomechanical modeling techniques were then employed for further calibration. The uncertainties associated with the subsurface geological modeling, leaking aquifer boundaries, reservoir heterogeneity, rock thermal, petrophysical, and geomechanical properties were considered for both isothermal and non-isothermal conditions.
Processing of DTS (Distributed Temperature Sensing) data from both injection and observation wells indicated dynamic perturbations in subsurface temperature due to injection operations. Geological characterization, performed through high-resolution 3D seismic images, core, and log data, and the existence of a leaking aquifer, were found to have significant impacts on CO2 plume evolution. Through history matching process of non-isothermal flow simulation, for both injector and observation wells, the extent of the cold region was estimated, and found to be mainly controlled by rock thermal properties, permeability, and injection rate. Our analysis suggested that cold temperature front was limited to near-wellbore region due to substantial heat loss by conduction, besides radial decay of convective flow.
Further non-isothermal coupled simulations indicated a large, but near-wellbore-limited reduction in effective horizontal stresses, induced by cold CO2 injection. Employing different values of thermal expansion coefficients, local potential open-mode fractures were observed; however, fracturing of entire formation was not experienced. This phenomenon was associated with local permeability enhancement, and potential improvement in CO2 injectivity. A comparison of isothermal and non-isothermal analyses on reservoir performance during CO2 injection was lastly provided.
Our analysis of subsurface injection and coupled processes in relation to geologic CO2 sequestration delivers critical insights on how and under what conditions these non-isothermal effects are generated. This ultimately provides a predictive tool to better characterize the reservoir behaviour, injectivity issues, and spatial location of a subsurface CO2 plume.
The seismic inversion method using the seismic onset times has shown great promise for integrating real- continuous seismic surveys for updating geologic models. However, due to the high cost of seismic surveys, such frequent seismic surveys are not commonly available. In this study, we focus on analyzing the impact of seismic survey frequency on the onset time approach, aiming to extend the advantages of onset time approach when infrequent seismic surveys are available.
To analyze the impact of seismic survey frequency on the onset time approach, first, we conduct a sensitivity analysis based on the frequent seismic survey data (over 175 surveys) of steam injection in a heavy oil reservoir (Peace River Unit) in Canada. The calculated onset time maps based on seismic survey data sampled at various time intervals from the frequent data sets are compared to examine the need and effectiveness of interpolation between surveys. Additionally, we compare the onset time inversion with the traditional seismic amplitude inversion and quantitatively investigate the nonlinearity and robustness for these two inversion methods.
The sensitivity analysis shows that using interpolation between seismic surveys to calculate the onset time an adequate onset time map can be extracted from the infrequent seismic surveys. This holds good as long as there are no changes in the underlying physical mechanisms during the interpolation period. A 2D waterflooding case demonstrates the necessity of interpolation for large time span between the seismic surveys and obtaining more accurate model update and efficient data misfit reduction during the inversion. The SPE Brugge benchmark case shows that the onset time inversion method obtains comparable permeability update as the traditional seismic amplitude inversion method while being much more efficient. This results from the significant data reduction achieved by integrating a single onset time map rather than multiple sets of amplitude maps. The onset time approach also achieves superior convergence performance resulting from its quasi-linear properties. It is found that the nonlinearity of the onset time method can be smaller than that of the amplitude inversion method by several orders of magnitude.
Objectives/Scope: In order to maximize the recovery of hydrocarbons from liquids rich shale reservoir systems, the cause and effect relationships between production and the stimulation methods need to be clearly understood. In this study, we utilize multivariate regression models to narrow down the variables in flow simulation models and their range. We then use the flow simulation model to understand the fractured well production behavior and field wide well performance in a liquids rich petroleum system in the Duvernay Basin.
Methods, Procedures, Process: Statistical models assume no physical relationship between the model parameters and the response variable, which in this case is produced volumes over a period of time. On the other hand, simulation studies incorporate physical mechanisms of flow to model and predict the production behavior. The simulation models, however, fall short of incorporating all the mechanisms contributing to the production behavior in the complex shale gas reservoir. Thus there is a need for integration of statistical approaches of understanding production behavior along with physics based model and simulation approach. We use the statistical methods to identify the important physical mechanisms that control the production.
Results, Observations, Conclusions: Multivariate linear regression analysis of the 6 month produced volume and its relationship with parameters such as fracture fluid volumes used, proppant weight placed, number of stages fractured provides a model with reasonably good correlation. The 6 month produced volumes correlate with large proppant weights, lower fluid placements and greater density of fracture stages. Use of Random Forests machine learning algorithm on the dataset confirms that the total proppant placed, well length completed with fractures have high importance coefficients. In order to examine the well performance using full physical models, fractured well simulations are performed on particular wells using the trilinear model. The trilinear model predictions are then compared against other production analyses and the regression model results for consistency. The models showed that in the absence of stress dependent permeability, the production forecast was much higher. Thus, stress dependent permeability appears to be an important factor in the modeling and prediction of production from liquids rich shale reservoirs.
Novel/Additive Information: In this study we describe a method to understand the production data from a liquids rich shale reservoir, by integrating multivariate linear regression analysis, machine learning algorithms along with physical model simulations. The results are novel and offer a method to validate either approach to understand cause and effect relationships. This approach may be classified as a new hybrid modeling workflow that may potentially be used to optimize stimulation techniques in liquids rich shale reservoirs.
Hirschmiller, John (GLJ Petroleum Consultants Ltd.) | Biryukov, Anton (Verdazo Analytics) | Groulx, Bertrand (Verdazo Analytics) | Emmerson, Brian (Verdazo Analytics) | Quinell, Scott (GLJ Petroleum Consultants Ltd.)
This machine learning study incorporates geoscience and engineering data to characterize which geological, reservoir and completion data contribute most significantly to well production performance. A better understanding of the key factors that predict well performance is essential in assessing the commercial viability of exploration and development, in the optimization of capital spending to increase rates of return, and in reserve and resource evaluations.
Machine learning models provide an objective, analytical means to interpret large, complex datasets. Generally, such models demand large databases of consistently evaluated data. As geological data is interpretive, often varying from one geologist to another, or from one pool to another, it can be difficult to incorporate geological data into regional machine learning models. Consequently, efforts to use machine learning in the oil and gas industry to predict well performance are often focused exclusively on engineering completion technology. However, this case study has utilized a regional geological Spirit River database with consistent petrophysical evaluation methodology across the entire play. This geological database is complemented with public completion and fracture data and production data to build predictive models using inputs from all subsurface disciplines.
Redundancies in the data were identified and removed. Features explaining a significant proportion of the variance in production were also removed if their effect was captured by more fundamental, correlated features that were more straightforward to interpret. The dataset was distilled to 13 key features providing predictions with a similar precision to those obtained using the full-featured dataset.
The thirteen features in this case study are a combination of geological, reservoir and completion data, underlining that an approach integrating both geoscience and engineering data is vital to predicting and optimizing well performance accurately for future wells.