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The first objective of this work is to determine the volume of hydrocarbon that can be moved from Resources other than Reserves (ROTR) to Reserves, or from Proved Undeveloped Reserves (PUD) to Reserves based on well placement. The second objective is to create a model that incorporates the production history and forecasted estimated ultimate recovery (EUR), in this case by implementing multi-segment decline curve analysis (DCA) as presented in URTeC 336 (
The first portion of this work involves running a sensitivity analysis to determine the spatial well relationships that may trigger movements in certain regulatory frameworks. A successful well may promote the offsetting 2P wells to PUD wells. We incorporate the methodology in
In the second part of this work, we create a model which includes the production history and the forecasted EURs. As time moves forward, continuity and consistency must be maintained across the model. Assume the following scenario: we plan to move a volume "
Reservoir simulation is becoming a standard practice for oil and gas companies, helping with decision making, reducing reservoir characterization uncertainties, and better manage hydrocarbon resources. The reservoir model sizes can reach multi-billion grid cells, which led Saudi Aramco to develop an in-house massively parallel reservoir simulator, as well as a pre- and post-reservoir simulation environment [Dogru et al. 2002]. Compared to structured grid modeling, unstructured grid modeling and billon cell pre- and post-simulation processing of reservoir simulation provides engineers with advanced modeling capabili- ties to represent complex well geometries and near wellbore modeling. Mapping between structured and unstructured (2.5D) domains is not a straightforward task. The indexing in unstructured grids makes cre- ating property modifiers, do near wellbore modeling, and local grid refinement (LGR) difficult.
We present a developed workflow to automatically transform the modeling of property modifiers, near wellbore modeling and LGR between the structured and unstructured domains. Several Computational Geometry algorithms were developed for efficiency and accuracy, which preprocess the corners of top layer cells into data structures. To map regions of interest (ROIs) between domains, the algorithms find all corner points inside them. The regions are translated using the algorithms and the results are exported in the unstructured format. The two challenges are that the number of corner points is massive, therefore, a brute-force search even for a simple ROI is expensive, and irregular ROIs result in very costly search complexity. We address these by preprocessing the input data in the form of range trees. We also propose a free-shape polygonal search strategy to find all corner points in the ROI.
The range tree algorithm provided a fast and robust workflow to perform the transformation from struc- tured to unstructured gridding domains, while providing ease of use with a visual component, to aid with property transformation, near wellbore modeling and LGR. The algorithm’s performance was measured using the time complexity of the preprocessing time, query time, and the space complexity. The range tree approach is fast compared to the other approaches, requiring only O(log(n)+k) operations, compared to the O(n) of linear search. It takes a costly O(nlog(n)) time to preprocess the data into the range tree, however, that is a one-time cost, as well as requiring O(nlog(n)) space in memory. This work is a major milestone to promote and support the unstructured grid modeling approach for large- and small-scale res- ervoir simulation models. The algorithm will provide engineers with a simplified workflow and smooth transitioning, allowing advanced capabilities to model complex well geometries and near wellbore mod- eling, while preserving complex geological features. In addition, this algorithm provides the building blocks in facilitating the migration and conversion of existing structured simulation models.
We have seen that two data sets can have the same univariate statistics, yet have very different spatial properties (Figure 1). The complex attributes we deal with in the petroleum industry can be described by random functions that are combinations of regionalized and random variables. Regionalized variable theory is based on the statistics of the RV,    which differs from ordinary scalar random variables in its spatial continuity, yet still possesses the usual distribution statistics, such as mean and variance. The RV also differs in that it has a defined location. Two realizations (measurements) of an RV that differ in spatial location display in general a nonzero correlation; however, successive realizations of an ordinary scalar random variable are uncorrelated.
Summary Proper characterization of heterogeneous rock properties and hydraulic fracture parameters is essential for optimization of well spacing and reliable estimation of estimated ultimate recovery (EUR) in unconventional reservoirs. We propose a novel reservoir model parameterization method to reduce the number of unknowns, regularize the ill-posed problem, and enhance the efficiency of history matching of unconventional reservoirs. Typically, hydraulic fractures are represented with much higher resolution through local grid refinements compared to the matrix properties. In our approach, the spatial property distribution of both matrix and fractures is represented using a few parameters via a linear transformation with multiresolution basis functions. The parameters in transform domain are then updated during model calibrations, substantially reducing the number of unknowns. The multiresolution basis functions are constructed by using Eigen-decomposition of an adaptively coarsened grid Laplacian corresponding to the data resolution. Higher property resolution at the area of interest through the adaptive resolution control while keeping the original grid structure improves quality of history matching, reduces simulation runtime, and improves the efficiency of history matching. We demonstrate the power and efficacy of our method using synthetic and field examples. First, we illustrate the effectiveness of the proposed multiresolution parameterization by comparing it with traditional methods. For the field application, an unconventional tight oil reservoir model with a multistage hydraulic fractured well is calibrated using bottomhole pressure and water cut history data. The hydraulic fractures as well as the stimulated reservoir volume (SRV) near the well are represented with higher grid resolution. The calibrated ensemble of models are used to obtain bounds of production forecast. Our proposed method is designed to calibrate reservoir and fracture properties with higher resolution in regions that have improved data resolution and higher sensitivity to the well performance data, for example the SRV region and the hydraulic fractures. This leads to a fast and efficient history matching workflow and enables us to make optimal development/completion plans in a reasonable time frame.
As data computing and big data driven analytics become more prevalent in a number of spatial industries, there is increasing need to quantify and communicate uncertainty with those data and resulting spatial analytical products. This has direct implication in oil & gas exploration and development where big data and data analytics continue to expand uses and applications of spatial and spatio-temporal data in the industry without providing for effective communication of spatial uncertainty. The result is that communications and inferences made using spatial data visuals lack crucial information about uncertainty and thus present a barrier to accurate and efficient decision making. With increasing cost awareness in oil & gas exploration and development, there is urgent need for methods and tools that help to objectively define and integrate uncertainty into business decisions.
To address this need, the Variable Grid Method (VGM) has been developed for simultaneous communication of both spatial patterns and trends and the uncertainty associated with data or their analyses. The VGM utilizes varying grid cell sizes to visually communicate and constrain the uncertainty, creating an integrated layer that can be used to visualize uncertainty associated with spatial, spatio-temporal data or data-driven products.
In this paper, we detail the VGM approach and demonstrate the utility of the VGM to intuitively quantify and provide cost-effective information about the relationship between uncertainty and spatial data. This allows trends of interest to be objectively investigated and target uncertainty criteria defined to drive optimal investment in improved subsurface definition. Examples are presented to show how the VGM can thus be used for efficient decision making in multiple applications including geological risk evaluation, as well as to optimize data acquisition in exploration and development.
Today, uncertainty, if it is provided at all, is generally communicated using multiple independent visuals, aggregated in final displays, or omitted altogether. The VGM provides a robust method for quantifying and representing uncertainty in spatial data analyses, offering key information about the analysis, but also associated risks, both of which are vital for making prudent business decisions in oil & gas exploration and development.
Deposits of heavy oil and natural bitumen have been long-discovered in the Dahomey basin south-western Nigeria. However, inconsistency in estimates of volumes of hydrocarbon contained in these deposits has inhibited commercial interest in the deposits. The inconsistency is attributable to the little or no consideration for spatial variability in those studies. This work is therefore motivated by the need for spatially-coherent geomodels leading to reliable volumetric estimates. An existing database of porosity, depth-to-top and thickness attributes of a section of the deposits located at Agbabu is the subject of this work.
This work conducted exploratory spatial data analysis (ESDA) as well as empirical variogram estimation, interpretation and modeling of the attributes. Here, the estimation and interpretation of empirical variogram faced a number of challenges with potentials to render the estimates uninterpretable, unstable and inconsistent with geologic information. These include presence of spatial outlier data, clusteredness of variogram clouds, data paucity, and irregular distribution of point-pairs on variogram clouds. Spatial outliers were either removed or correlated with existing geologic information. The clusteredness issues were resolved using a machine-learning – aided variogram estimation technique recently proposed. Variogram cloud binning approach was deployed to handle irregular distribution of point-pairs. In attempting to deploy an automatic fitting algorithm, cases of insufficient empirical points leading to lack of convergence were encountered. Such cases were resolved by adopting a combination of manual and automatic fitting approaches.
Ultimately, this work presents a three-dimensional anisotropic (zonal) porosity variogram model and two-dimensional anisotropic (geometric) models for the depth-to-top and thickness variograms. These models are suitable inputs to spatial interpolation algorithms in generating maps of these volumetric attributes.
Frac fluid delivery is selective in effect, so must fracture models. Here, a physics-based analytical model, called nine-grain model, is presented for production forecasting in multifrac horizontal wells in unconventional reservoirs, where the utilized formulation inherently enables defining three-dimensional non-uniform SRVs, selective frac-hits, and pressure- and time-dependent permeabilities. The model is validated by constructing case studies of liquid and gas reservoirs and comparing the results with numerical simulations. In cases with both production history and fracing-induced microseismic data available, the SRV's spatial structure is extracted using a hybrid four-level straight-line technique that links volumetric RTA estimations to morphometric microseismic analysis and entails plots of plasticity, diffusivity, flowing material balance and early linear flow. By applying our model to an oil well in Permian Basin, we demonstrate that the knowledge gained from the coupled microseismic-RTA contributes to resolving the non-uniqueness of RTA solutions. The proposed reservoir modeling procedure enables efficient incorporation of microseismic interpretations in modern RTA while honoring the SRV space-time variability, thus facilitates informed decision making in spacing design of wells and perforation clusters.
Frac-hits. A frac-hit can be defined as observing a perturbation in the well production rate and/or pressure that is induced by a child offset (or an infill) well, usually triggered by pressure sinks created around parent wells or high permeability lithofacies. A frac-hit that temporarily alters the parent well productivity is called a communication frac-hit, and those with long-term effects, generally caused by fracture interference, are referred as interference frac-hits. A frac-hit may also compromise the productivity of the child well itself since the existing pressure sinks distribute the fracing energy in a larger area and might lead to an asymmetric fracture growth around the child well. Besides the parent well operational condition, the microseismic monitoring of fracing can potentially indicate interference frac-hits as it reveals fracture overlaps and any preferential fracture dilation towards existing wells. Depending on the rock and fluid properties, well age, parent-child horizontal and vertical distances, and the spatial extent of Stimulated Reservoir Volume (SRV), the constructive (Esquivel and Blasingame 2017) or destructive (King et al. 2017, Ajani and Kelkar 2012) effects of frac-hits can be experienced by fractures, SRV or the entire drainage volume (stimulated and non-stimulated zones), usually by impacting rock multiphase fluid interfacial arrangements and/or changing dimensions of conductive fractures. Aside from prevention, thoroughly reviewed by Whitfield et al. (2018), it is essential to incorporate frac-hits into production forecasting models, which to date, is not yet as straightforward as their detection. Both types of frac-hits cause a change in the well productivity over time which is not necessarily correlated with pressure, and hence, complicate the reservoir modeling process.
Martini, Brigette (Corescan Inc.) | Bellian, Jerome (Whiting Petroleum Corporation) | Katz, David (Encana Corporation) | Fonteneau, Lionel (Corescan Pty Ltd) | Carey, Ronell (Corescan Pty Ltd) | Guisinger, Mary (Whiting Petroleum Corporation) | Nordeng, Stephan H. (University of North Dakota)
Hyperspectral core imaging studies of the Bakken-Three Forks formations over the past four years has revealed non-destructive, high resolution, spatially relevant insight into mineralogy, both primary and diagenetically altered that can be applied to reservoir characterization. While ‘big’ data like co-acquired hyperspectral imagery, digital photography and laser profiles can be challenging to analyze, synthesize, scale, visualize and store, their value in providing mineralogical information, structural variables and visual context at scales that lie between (and ultimately link) nano and reservoir-scale measurements of the Bakken-Three Forks system, is unique.
Simultaneous, co-acquired hyperspectral core imaging data (at 500 μm spatial resolution), digital color photography (at 50 μm spatial resolution) and laser profiles (at 20 μm spatial and 7 μm vertical resolution), were acquired over 24 wells for a total of 2,870 ft. of core, seven wells of which targeted the Bakken-Three Forks formations. These Bakken-Three Forks data (~5.5 TB) represent roughly 175,000,000 pixels of spatially referenced mineralogical data. Measurements were performed at a mobile Corescan HCI-3 laboratory based in Denver, CO, while spectral and spatial analysis of the data was completed using proprietary in-house spectral software, offsite in Perth, WA, Australia. Synthesis of the spectral-based mineral maps and laser-based structural data, with ancillary data (including Qemscan, XRD and various downhole geophysical surveys) were completed in several software and modelling platforms.
The resulting spatial context of this hyperspectral imaging-based mineralogy and assemblages are particularly compelling, both in small scale micro-distribution as well as borehole scale mineralogical distributions related to both primary lithology and secondary alteration. These studies also present some of the first successful measurement and derivation of lithology from hyperspectral data. Relationships between hyperspectral-derived mineralogy and oil concentrations are presented as are separately derived structural variables. The relationship between hyperspectral-based mineralogy to micro-scale reservoir characteristics (including those derived from Qemscan) were studied, as were relationships to larger-scale downhole geophysical data (resulting in compelling correlations between variables of resistivity and hyperspectral-mineralogy). Finally, basic Net-to-Gross calculations were completed using the hyperspectral imaging data, thereby extending the use of such data from geological characterizations through to resource estimations.
The high-fidelity mineralogical maps afforded by hyperspectral core imaging have not only provided new geological insight into the Bakken-Three Forks formations, but ultimately provide improved well completion designs in those formations, as well as a framework for applying the technology to other important unconventional reservoir formations in exploration and development. The semi-automated nature of the technology also ushers in the ability to consistently and accurately log mineralogy from multiple wells and fields globally, allowing for advanced comparative analysis.
Proper characterization of heterogeneous rock properties and hydraulic fracture parameters is essential for optimizing well spacing and reliable estimation of EUR in unconventional reservoirs. High resolution characterization of matrix properties and complex fracture parameters requires efficient history matching of well production and pressure response. We propose a novel reservoir model parameterization method to reduce the number of unknowns, regularize the ill-posed problem and enhance the efficiency of history matching of unconventional reservoirs.
Our proposed method makes a low rank approximation of the spatial distribution of reservoir properties taking into account the varying model resolution of the matrix and hydraulic fractures. Typically, hydraulic fractures are represented with much higher resolution through local grid refinements compared to the matrix properties. In our approach, the spatial property distribution of both for matrix and fractures is represented using a few parameters via a linear transformation with multiresolution basis functions. The parameters in transform domain are then updated during model calibrations, substantially reducing the number of unknowns. The multiresolution basis functions are constructed by eigen-decomposition of an adaptively coarsened grid Laplacian corresponding to the data resolution. High property resolution at the area of interest through the adaptive resolution control while keeping the original grid structure improves quality of history matching, reduces simulation runtime and improves the efficiency of history matching.
We demonstrate the power and efficacy of our method using synthetic and field examples. First, we illustrate the effectiveness of the proposed multiresolution parameterization by comparing it with traditional method. For the field application, an unconventional tight oil reservoir model with a multi-stage hydraulic fractured well is calibrated using bottom-hole pressure and water cut history data. The hydraulic fractures as well as the stimulated reservoir volume (SRV) near the well are represented with higher grid resolution. In addition to matrix and fracture properties, the extent of the SRV and hydraulic fractures are also adjusted through history matching using a Multiobjective Genetic Algorithm. The calibrated ensemble of models are used to obtain bounds of production forecast.
Our proposed method is designed to calibrate reservoir and fracture properties with higher resolution in regions that have improved data resolution and higher sensitivity to the well performance data, for example the SRV region and the hydraulic fractures. This leads to a fast and efficient history matching workflow and enables us to make optimal development/completion plans in a reasonable time frame.