Petrophysical Uncertainty Research Articles

Exploring the Impact of Petrophysical Uncertainties on Recoverable Reserves: A Case Study

Reliable estimation of critical parameters such as hydrocarbon pore volume, water saturation, and recovery factor are essential for accurate reserve assessment. The inherent uncertainties associated with these parameters encompass a reasonable range of estimated recoverable volumes for single accumulations or projects. Incorporating this uncertainty range allows for a comprehensive understanding of potential outcomes and associated risks. In this study, we focus on the oil field located in the northern part of Iraq and employ a Monte Carlo based petrophysical uncertainty modeling approach. This method systematically considers various sources of error and utilizes effective interpretation techniques. Leveraging the current state of available data, our approach generates a wide range of theoretically possible results. Furthermore, establishing a set of probabilities to indicate the likelihood of each possible outcome is of utmost importance. By implementing this approach, we aim to enhance reserve assessments by accounting for petrophysical uncertainties, thereby providing decision makers with valuable insights into the range of possible outcomes and associated risks. This study contributes to a more robust understanding of recoverable reserves and supports informed decision making in the oil and gas industry.

Comparative Study of CO2 Storage Capacity Estimation in Depleted Oil & Gas Reservoir: A Case Study in Vermillion Basin Gulf of Mexico

CO2 emissions rates have seen an exponential growth from the 19th century up till date, if no drastic measures and plans are implemented to prevent this exponential growth the consequence will be devastating. The notion of achieving net-zero greenhouse gas emissions gained prominence through the Paris Agreement, a groundbreaking accord reached at the United Nations Climate Change Conference. This agreement was devised to mitigate the impact of greenhouse gas emissions. To execute the net-zero CO2 emission plan, the USDOE has set a new goal to remove gigatons of carbon dioxide (CO2 ) from the atmosphere and durably store it for less than $100/ton of net CO2 -equivalent. Making such a goal a reality requires an accurate estimation of CO2 storage capacity for the successful implementation of Carbon Capture and Storage (CCS) technologies, and the assessment of the impact of CCS to the reduction of CO2 emissions. Hence this paper serves as a template for accurately estimating CO2 storage capacity in depleted saturated oil reservoirs with initial gas cap using three approaches: Volumetric, Production and Correlation-based methods and compares the accuracy of the estimates. A case study was conducted on a depleted VR273_Q combination sand in the Vermillion Basin, Gulf of Mexico (GOM). The deterministic and stochastic (P50) CO2 storage capacity estimates from the Volume-based method are 1.21 million tonnes (Mt) and 1.23 Mt respectively, while the deterministic CO2 storage capacity estimates from the Production and Correlationbased method are 1.32 Mt and 1.41 Mt respectively. All three approaches showed similar results, with little deviations attributed to petrophysical uncertainties arising from data gaps i.e., absence of well logs to key wells. However, these uncertainties are captured by Stochastic (P90) CO2 storage capacity estimates of 1.47 Mt from the Volume-based method. Although the Correlation-based approach slightly overestimates the CO2 storage capacity, it can be used as a starting point for quick estimation as it only requires production data which are readily available on various databases for GOM. Finally, through this paper, opportunities for concerned agencies to make well-informed energy-related policies and business decisions are made possible.

Solving Geophysical Inversion Problems with Intractable Likelihoods: Linearized Gaussian Approximations Versus the Correlated Pseudo-marginal Method

A geophysical Bayesian inversion problem may target the posterior distribution of geological or hydrogeological parameters given geophysical data. To account for the scatter in the petrophysical relationship linking the target parameters to the geophysical properties, this study treats the intermediate geophysical properties as latent (unobservable) variables. To perform inversion in such a latent variable model, the intractable likelihood function of the (hydro)geological parameters given the geophysical data needs to be estimated. This can be achieved by approximation with a Gaussian probability density function based on local linearization of the geophysical forward operator, thereby, accounting for the noise in the petrophysical relationship by a corresponding addition to the data covariance matrix. The new approximate method is compared against the general correlated pseudo-marginal method, which estimates the likelihood by Monte Carlo averaging over samples of the latent variable. First, the performances of the two methods are tested on a synthetic test example, in which a multivariate Gaussian porosity field is inferred using crosshole ground-penetrating radar first-arrival travel times. For this example with rather small petrophysical uncertainty, the two methods provide near-identical estimates, while an inversion that ignores petrophysical uncertainty leads to biased estimates. The results of a sensitivity analysis are then used to suggest that the linearized Gaussian approach, while attractive due to its relative computational speed, suffers from a decreasing accuracy with increasing scatter in the petrophysical relationship. The computationally more expensive correlated pseudo-marginal method performs very well even for settings with high petrophysical uncertainty.

The design of an open-source carbonate reservoir model

This work presents a new open-source carbonate reservoir case study, the COSTA model, that uniquely considers significant uncertainties inherent to carbonate reservoirs, providing a far more challenging and realistic benchmarking test for a range of geo-energy applications. The COSTA field is large, with many wells and large associated volumes. The dataset embeds many interacting geological and petrophysical uncertainties in an ensemble of model concepts with realistic geological and model complexity levels and varying production profiles. The resulting number of different models and long run times creates a more demanding computational challenge than current benchmarking models. The COSTA model takes inspiration from the shelf-to-basin geological setting of the Upper Kharaib Member (Early Cretaceous), one of the most prolific aggradational parasequence carbonate formations sets in the world. The dataset is based on 43 wells and the corresponding fully anonymised data from the northeastern part of the Rub Al Khali basin, a sub-basin of the wider Arabian Basin. Our model encapsulates the large-scale geological setting and reservoir heterogeneities found across the shelf-to-basin profile, into one single model, for geological modelling and reservoir simulation studies. The result of this research is a semi-synthetic but geologically realistic suite of carbonate reservoir models that capture a wide range of geological, petrophysical, and geomodelling uncertainties and that can be history-matched against an undisclosed, synthetic ‘truth case’. The models and dataset are made available as open-source to analyse several issues related to testing new numerical algorithms for geological modelling, uncertainty quantification, reservoir simulation, history matching, optimization and machine learning. Supplementary material: supplementary data associated with this article can be found in the data repository for the COSTA model available at https://doi.org/10.17861/6e36e28d-50d9-4e31-9790-18db4bce6e5d and supplementary material is available at https://doi.org/10.6084/m9.figshare.c.5823571

Evaluating Petrophysical Properties and Volumetrics Uncertainties of Sand Injectite Reservoirs – Norwegian North Sea

Sand injectites on the Norwegian Continental Shelf have proven their commercial significance. Some are already producing, e.g., Volund, Viper, Balder, Ringhorne, and Kobra Fields, while others such as in production licenses (PL) 340 and 869 have recently been discovered and appraised. Extensive literature on the geology of sand injectites has been published (e.g., Jenssen et al., 1993; Jolly and Lonergan, 2002; Huuse et al., 2003; Hurst et al., 2005). However, few references are available on the petrophysical and geophysical aspects of sand injectite reservoirs. This paper discusses the petrophysical properties of sand injectite facies, dykes, sills, and brecciated sands, along with their identification from seismic data. A perception that volumetrics of sand injectite reservoirs cannot be reliably evaluated is assessed. Sand injectites in PL 340 and 869 were interpreted as remobilized sands from the Hermod and Heimdal Formations of Paleocene age injected into the overlying Balder Formation and Hordaland Group mudstones of Eocene age. The mudstones acted as a seal, forming an intrusive stratigraphic trap. The trap geometry varied locally depending upon the dyke and sill geometries of the sandstone. Dykes had large vertical reach with the corresponding high-hydrocarbon column, while sills had low-vertical relief with large lateral extent. Intervals of brecciated sands were also observed within the injectite complex, especially where sands were thin. These brecciated sands contained large amounts of angular mudstone clasts of different dimensions suspended in an overall sandy matrix. Close examination of cored dykes made it possible to observe this, while it might not be as obvious when looking at bulk well logs. Petrophysical-log responses for clean sills and dykes behaved the same way as they would in a clean sandstone reservoir. If sills and dykes were very thin, they would also risk not being counted as net or pay (Suau et al., 1984; Dromgoole et al., 2000; Flølo et al., 2000;). Such errors can impact in-place volumes in a significant way. Sills appeared as blocky clean sand on logs, but it was difficult to differentiate a dyke from a sill or thin sands using logs. Dykes are high-angle features and are identified either by core studies or borehole images when intersected by a well or, if large enough, observable on seismic. Brecciated sand intervals appeared with cm-to-dm-scale mudstone clasts suspended in sand with approximately 40 to 60% net to gross. Log responses over these intervals indicated shaly sand or thin sands. Resistivity and thermal neutron porosity logs were highly affected by the shale clasts. For this reason, a fractional net/gross interpretation technique was used to evaluate the sand content and hydrocarbon pore volume. To further verify these results, they were compared to observations directly on the core. To qualify to what extent petrophysical logs and interpreted products thereof can be relied on to evaluate hydrocarbon volumes of sand injectite reservoirs, a high-resolution petrophysical interpretation was generated using a computerized tomography (CT) scanned core image. Core image sand counting and image-derived high-resolution bulk density logs with shale-corrected resistivity were used. Results of this high-resolution interpretation featured an excellent match with routine core analysis data and manual core observations in the core laboratory. The fractional net/gross method used is the modified Thomas-Stieber method (Johansen et al., 2018). Its results compared well to the high-resolution CT-scan image results and better evaluated the hydrocarbon pore volume of sand facie compared to the conventional bulk formation evaluation approach. This result confirms that the Thomas-Stieber method can be used for brecciated rocks, which leads to some useful recommendations on how to best log and perform a petrophysical evaluation in such reservoirs.

The impact of heterogeneity on the capillary trapping of [formula omitted] in the Captain Sandstone.

A significant uncertainty which remains for CO2 sequestration, is the effect of natural geological heterogeneities and hysteresis on capillary trapping over different length scales. This paper uses laboratory data measured in cores from the Goldeneye formation of the Captain D Sandstone, North Sea in 1D numerical simulations to evaluate the potential capillary trapping from natural rock heterogeneities across a range of scales, from cm to 65m. The impact of different geological realisations, as well as uncertainty in petrophysical properties, on the amount of capillary heterogeneity trapping is estimated. In addition, the validity of upscaling trapping characteristics in terms of the Land trapping parameter is assessed. The numerical models show that the capillary heterogeneity trapped CO2 saturation may vary between 0 and 14% of the total trapped saturation, depending upon the geological realisation and petrophysical uncertainty. When upscaling the Land model from core-scale experimental data, using the maximum experimental Land trapping parameter could increase the expected heterogeneity trapping by a factor of 3. Conversely, depending on the form of the imbibition capillary pressure curve used in the numerical model, including capillary pressure hysteresis may reduce the heterogeneity trapping by up to 70%.

Tutorial: Petrophysics of Thinly Bedded Formations

This paper was written in response to a personal request from the Editor to contribute a “tutorial” on thin-bed petrophysics. The paper is not intended to be an exhaustive review of all the work that has been done regarding thin-bed petrophysics. Instead, it is an introduction to the different types of methods available, with some discussion on the strengths and weaknesses of each. Thin beds have posed problems for petrophysicists for many decades, mostly because conventional interpretation techniques in thin beds tend to overestimate water saturation, owing to the very low resistivities often seen in hydrocarbon-bearing intervals. There are a number of different techniques available to the petrophysicist for evaluating thin beds, including conventional “bulk volume” techniques, various types of high-resolution modeling, and different low-resolution modeling techniques, both with and without triaxial resistivity measurements. There is no single best technique to use. Instead, the most appropriate method is dependent on the formation complexity and the types of data available. Generally, it is very useful to have triaxial resistivity measurements to understand the formation anisotropy better. However, in some cases, simple “triple-combo” logging suites may be sufficient, provided the interpretation can be improved and verified with core data. In other cases, complex formation modeling is required to provide the best results with low uncertainty. If using either low- or high-resolution modeling, the reporting of evaluation results is not straightforward. Net reservoir and net pay cutoffs require special consideration, and the results are specific to the formation components rather than the bulk volume formation, as they are with conventional reservoir summaries. Furthermore, the quantification of petrophysical uncertainties in formation modeling of thinly bedded formations is better done using scenario modeling rather than Monte Carlo processing, which is commonly applied with bulk volume techniques.

Estimation of Initial Hydrocarbon Saturation Applying Machine Learning Under Petrophysical Uncertainty

SummaryThe initial hydrocarbon saturation has a major effect on field-development planning and resource estimation. However, the bases of the initial hydrocarbon saturation are indirect measurements from spatially distributed wells applying saturation-height modeling using uncertain parameters. Because of the multitude of parameters, applying assisted-matching methods requires trade-offs regarding the quality of objective functions used for the various observed data. Applying machine learning (ML) in a Bayesian framework helps overcome these challenges. In the present study, the methodology is used to derive posterior parameter distributions for saturation-height modeling honoring the petrophysical uncertainty in a field. The results are used for dynamic model initialization and will be applied for forecasting under uncertainty. To determine the dynamic numerical model initial hydrocarbon saturation, the saturation-height model (SHM) needs to be conditioned to the petrophysically interpreted logs. There were 2,500 geological realizations generated to cover the interpreted ranges of porosity, permeability, and saturations for 15 wells. For the SHM, 12 parameters and their ranges were introduced. Latin hypercube sampling was used to generate a training set for ML models using the random forest algorithm. The trained ML models were conditioned to the petrophysical log-derived saturation data. To ensure a fieldwide consistency of the dynamic numerical models, only parameter combinations honoring the interpreted saturation range for all wells were selected. The presented method allows for consistent initialization and for rejection of parameters that do not fit the observed data. In our case study, the most-significant observation concerns the posterior parameter-distribution ranges, which are narrowed down dramatically, such as the free-water-level (FWL) range, which is reduced from 645–670 m subsea level (mSS) to 656–668 mSS. Furthermore, the SHM parameters are proved independent; thus, the resulting posterior parameter ranges for the SHM can be used for conditioning production data to models and subsequent hydrocarbon-production forecasting. Additional observations can be made from the ML results, such as the correlation between wells; this allows for interpreting groups of wells that have a similar behavior, favor the same combinations, and potentially belong to the same compartment.

Optimization Framework Improves Mariner Field Development

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 193883, “Robust Multiobjective Field Development Optimization for the Mariner Asset,” by Remus Gabriel Hanea, SPE, Ole Petter Bjorlykke, Yastoor Hasmi, and Tao Feng, Equinor, and Rahul Mark Fonseca, TNO, prepared for the 2019 SPE Reservoir Simulation Conference, Galveston, Texas, USA, 10–11 April. The paper has not been peer reviewed. The growing popularity of model-based optimization work flows has resulted in an increase in their application to field cases. In the complete paper, the authors present the challenges, results, and learnings from a 2-year robust multiobjective optimization application at the Mariner heavy-oil asset in the UK sector of the North Sea. The authors used the efficient stochastic simplex approximate gradient technique (StoSAG) to achieve optimization incorporating geological and petrophysical uncertainty. Depending on the problems, significant increases of between 5 and 20% in the expected value of the objective function were achieved. For the multiobjective optimization cases, nontrivial optimal strategies reduced gas production by 40% with less than 1% loss in the economic objective. Introduction To date, most studies of single- and multiobjective optimization have focused on a single study with a specific purpose. Very few studies have used these work flows in an operational setting (i.e., during the field-development-planning stage at an asset tackling a variety of•problems). Including uncertainty is an important step in decision making during field-development planning, especially because uncertainty is so extensive at this stage in the life cycle of a field. Considering a range of potential development scenarios that will provide the necessary tools to enable robust decisions is imperative. These scenarios should also account for the fact that different objectives often conflict with one another. Thus, there is a need for multiobjective optimization under uncertainty, which can be applied in an operational setting. Adapting model-based optimization work flows requires computational efficiency. While the adjoint-based gradient method is computationally very efficient, it is not suitably flexible to incorporate different types of control variables and requires access to the simulation source code as well as a significant implementation effort. Thus, in this paper, the authors use the StoSAG technique. They chose to use an approximate gradient method for the optimization instead of a derivative-free technique because the computational costs for derivative-free methods are usually higher when uncertainty, in terms of different model realizations, is considered. StoSAG is an approximate-gradient-based approach that has proven practical for optimization under uncertainty. A user must decide on the ratio of geological model realizations to control perturbations. The authors used a ratio of 1:1 for all experiments unless otherwise specified, making the number of control perturbations equal to the number of geological realizations. This is computationally the most-efficient approach for large-scale optimization under uncertainty when using high-fidelity, full-physics-simulation models. The objective function is the usual expression for (simple) net present value as used in life-cycle-optimization studies.

On the Field Estimation of Moisture Content Using Electrical Geophysics: The Impact of Petrophysical Model Uncertainty

AbstractThe spatiotemporal distribution of pore water in the vadose zone can have a critical control on many processes in the near‐surface Earth, such as the onset of landslides, crop yield, groundwater recharge, and runoff generation. Electrical geophysics has been widely used to monitor the moisture content (θ) distribution in the vadose zone at field sites, and often resistivity (ρ) or conductivity (σ) is converted to moisture contents through petrophysical relationships (e.g., Archie's law). Though both the petrophysical relationships (i.e., choices of appropriate model and parameterization) and the derived moisture content are known to be subject to uncertainty, they are commonly treated as exact and error‐free. This study examines the impact of uncertain petrophysical relationships on the moisture content estimates derived from electrical geophysics. We show from a collection of data from multiple core samples that significant variability in the θ(ρ) relationship can exist. Using rules of error propagation, we demonstrate the combined effect of inversion and uncertain petrophysical parameterization on moisture content estimates and derive their uncertainty bounds. Through investigation of a water injection experiment, we observe that the petrophysical uncertainty yields a large range of estimated total moisture volume within the water plume. The estimates of changes in water volume, however, generally agree within (large) uncertainty bounds. Our results caution against solely relying on electrical geophysics to estimate moisture content in the field. The uncertainty propagation approach is transferrable to other field studies of moisture content estimation.

Application of 3D static modeling for optimal reservoir characterization

Robi-1 field, located in the Central Swamp-I depositional belt of the Niger Delta, is largely typified by uncertainties due to complexity of reservoirs features, which resulted in difficulty in imaging and capturing all structural possibilities. These in turn affect the effectiveness of further exploration, appraisal, and development. This study was, therefore, undertaken to produce 3D structural and property models to better characterize, image and capture the reservoirs by integrating 3D seismic, logs and petrophysical parameters from the Robi-1 well. Two main reservoirs (A1 and A2) which contain bulk of the hydrocarbon in the Robi-1 field were identified, analyzed and evaluated. Attribute constrained reservoir models that capture possible structural and petrophysical uncertainties away from the well were generated to estimate hydrocarbon reserves as well as to determine optimal well placement. Reservoir structural models indicate that non-crossing East-West striking faults control the structural architecture, sealing the hydrocarbon fluids to the northern and southwestern part of the reservoir. Petrophysical results also show that both reservoirs have the comparable mean porosity and hydrocarbon saturation. Moreover, attribute analysis suggests that the northwestern region near reservoir A1 has a good hydrocarbon saturation and high porosity. The resulting models have assisted in proposing a deviated well to target the oil rim at A1 and the unknown hydrocarbon at A2.

Oil production uncertainty assessment by predicting reservoir production curves and confidence intervals from arbitrary proxy responses

Underground fluid flow in hydrocarbon reservoirs (or aquifers) is difficult to predict accurately due to geological and petrophysical uncertainties. To quantify that uncertainty, several spatial statistical methods are often used to generate an ensemble of subsurface models representing and sampling these uncertainties. However, to predict the uncertainties in terms of flow responses, one needs to run a forward flow simulator (often multiphase flow in transient state) on every model of this ensemble and this generally entails intractable computational costs. Approximate solutions (flow proxies) can help addressing this challenge but introduce physical simplifications whose impact on the uncertainty quantification is difficult to characterize. This paper proposes a workflow to assess the dynamic reservoir behavior uncertainties from an input ensemble of realizations sampling geological and geophysical uncertainties. Analytical reservoir production curves are estimated from proxy distances computed between all ensemble members and from a few accurate flow responses computed on a subset of the ensemble. A randomization process accounting for proxy quality and for model selection is used to assess confidence intervals about reservoir production quantile curves. The process can use both static and dynamic proxies and also permits to compare their efficiency. A case study on a real turbiditic reservoir shows the applicability of the method, and highlights the value of even a simple proxy to increase the confidence about future reservoir production.

Impact of petrophysical uncertainty on Bayesian hydrogeophysical inversion and model selection

Abstract Quantitative hydrogeophysical studies rely heavily on petrophysical relationships that link geophysical properties to hydrogeological properties and state variables. Coupled inversion studies are frequently based on the questionable assumption that these relationships are perfect (i.e., no scatter). Using synthetic examples and crosshole ground-penetrating radar (GPR) data from the South Oyster Bacterial Transport Site in Virginia, USA, we investigate the impact of spatially-correlated petrophysical uncertainty on inferred posterior porosity and hydraulic conductivity distributions and on Bayes factors used in Bayesian model selection. Our study shows that accounting for petrophysical uncertainty in the inversion (I) decreases bias of the inferred variance of hydrogeological subsurface properties, (II) provides more realistic uncertainty assessment and (III) reduces the overconfidence in the ability of geophysical data to falsify conceptual hydrogeological models.

On uncertainty quantification in hydrogeology and hydrogeophysics

Abstract Recent advances in sensor technologies, field methodologies, numerical modeling, and inversion approaches have contributed to unprecedented imaging of hydrogeological properties and detailed predictions at multiple temporal and spatial scales. Nevertheless, imaging results and predictions will always remain imprecise, which calls for appropriate uncertainty quantification (UQ). In this paper, we outline selected methodological developments together with pioneering UQ applications in hydrogeology and hydrogeophysics. The applied mathematics and statistics literature is not easy to penetrate and this review aims at helping hydrogeologists and hydrogeophysicists to identify suitable approaches for UQ that can be applied and further developed to their specific needs. To bypass the tremendous computational costs associated with forward UQ based on full-physics simulations, we discuss proxy-modeling strategies and multi-resolution (Multi-level Monte Carlo) methods. We consider Bayesian inversion for non-linear and non-Gaussian state-space problems and discuss how Sequential Monte Carlo may become a practical alternative. We also describe strategies to account for forward modeling errors in Bayesian inversion. Finally, we consider hydrogeophysical inversion, where petrophysical uncertainty is often ignored leading to overconfident parameter estimation. The high parameter and data dimensions encountered in hydrogeological and geophysical problems make UQ a complicated and important challenge that has only been partially addressed to date.

Bayesian rock classification and petrophysical uncertainty characterization with fast well-log forward modeling in thin-bed reservoirs

Rock typing is critical in deepwater reservoir characterization to construct stratigraphic models populated with static and dynamic petrophysical properties. Rock typing based on multiple well logs is subject to large uncertainty in thinly bedded reservoirs because true physical properties cannot be resolved by low-resolution logging tools due to shoulder-bed effects. We have introduced a new Bayesian approach that inherently adopts the scientific method of iterative hypothesis testing to perform rock typing by simultaneously honoring different logging-tool physics in a multilayered earth model. In addition to estimating the vertical distribution of rock types with maximum likelihood, the Bayesian method quantifies the uncertainty of rock types and the associated petrophysical properties layer by layer. Bayesian rock classification is performed with a fast sampling technique based on the Markov-chain Monte Carlo method, thereby enabling an efficient search of rock types to obtain the final results. We have used a fast linear iterative refinement method to simulate nuclear logs and a 2D forward modeling code to simulate array-induction resistivity logs. A rock-type distribution hypothesis is considered acceptable only when all the observed well logs are reproduced with forward modeling. In a field case of offshore deltaic gas reservoir, the Bayesian method differentiates rock types that exhibit subtle petrophysical variations due to grain size change. The new method provides more than 77% agreement between log- and core-derived rock types, whereas conventional deterministic methods achieve only 60% agreement due to the presence of thin beds and laminations. Even though large uncertainty is observed in thinly bedded and laminated zones, the Bayesian rock-typing method still yields rock types and petrophysical properties that agree well with core-plug measurements acquired in these layers. As a result, the overall correlation between log-derived permeability and core-measured permeability is improved by approximately 16% when compared with conventional deterministic methods.

Uncertainty in attribute-based inversion for quantification of the estimation of stock-tank oil initially in place in Kalol reservoir, Cambay Basin, India

Uncertainty is not an inherent feature of a reservoir; it is the result of lack of knowledge and understanding about the reservoir. Uncertainty can be modeled, but there is no objective measure of uncertainty. All geostatistical methods used in estimation of reservoir parameters are inaccurate; hence, modeling of “estimation error” in the form of uncertainty analysis is important. Uncertainty is associated with each process involved in geologic modeling, including data acquisition, data processing, interpretation, structural modeling, facies modeling, petrophysical modeling, and volume estimation, which affects the ability to understand the reservoir behavior and make reliable production forecasts and risk-free decisions. Hence, uncertainty analysis is a prerequisite measure for initial calculation of oil in place in any field. In the estimation of stock-tank oil initially in place (STOIIP) in Kalol reservoir, Cambay Basin, India, a geologic uncertainty study was initiated to identify and quantify the input parameters of greatest impact in the reservoir model. Results showed that the structural uncertainty has maximum impact and volume expansion factor has minimum impact in the estimation of stock-tank oil initially in place, whereas lithologic and petrophysical uncertainties have equal impact. Hence, during structural, facies, and petrophysical modeling, one has to take special precaution before finalizing the reserve calculation. Therefore, uncertainty and sensitivity analysis in the estimate of stock-tank oil initially in place should be applied on a routine basis because it greatly helps in improving fluid estimate from the field and ultimately the economics of the investments.

Geologic modeling and fluid-flow simulation of acid gas disposal in western Wyoming

The Moxa arch anticline is a regional-scale northwest-trending uplift in western Wyoming where geologic storage of acid gas (carbon dioxide, methane, nitrogen, hydrogen sulfide, ethane) is under consideration. Nugget Sandstone, a deep saline aquifer at depths exceeding 17,000 ft (5180 m), is a candidate formation. This study builds three-dimensional local- to regional-scale geologic and fluid-flow models for the Nugget Sandstone and its neighboring formations. Geologic and engineering characterization data were assembled and screened for accuracy. Using geostatistical simulations (first, sequential indicator simulation of facies, then the sequential Gaussian simulation of porosity []), the data were integrated to create a regional-scale geologic model from which a local-scale simulation model surrounding the proposed injection si te was extracted. Using this model, acid gas injection was simulated for 50 yr, followed by 1950 yr of monitoring. A sensitivity analysis was conducted, exploring the impact of geologic and engineering variables on model predictions. Results suggest that, at the simulation time scale, low dissolved solids in formation water, large gas-phase relative permeability (krg) hysteresis, and low vertical-to-horizontal intrinsic permeability (k) anisotropy all contribute to enhanced storage of acid gas in both residual (trapped) and dissolved forms. The parameter that exerts the largest control on gas storage is relative permeability hysteresis. However, given parameter uncertainty, the total predicted gas storage varies significantly. Prediction uncertainty increases in the order of dissolved gas, trapped gas, and mobile gas. In comparison, petrophysical uncertainty, as represented by multiple realizations, has limited impact on prediction, although future work is needed to expand the uncertainty analysis by developing alternative facies models for the storage formations.

Ranking Production Potential From Key Geological Drivers - Bakken Case Study

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper IPTC 14733, ’Ranking Production Potential Based on Key Geological Drivers - Bakken Case Study,’ by Robin S. Pilcher, Jessica McDonough Ciosek, Kelsey McArthur, John Hohman, and Peter J. Schmitz, SPE, Hess Corporation, prepared for the 2011 International Petroleum Technology Conference, Bangkok, Thailand, 15-17 November 2011. The paper has not been peer reviewed. Conference rescheduled to 7-9 February 2012. The Bakken play is a complex, low-porosity/-permeability system with variable source maturity, reservoir pressure and temperature, and fluid type. Target reservoirs are thin and variably affected by subtle structure, faults, or natural fractures. Many horizontal wells with multistage completions will be required to develop the Bakken play. A method is presented to assess the underlying production potential of the reservoirs on the basis of key geological drivers. With an extensive well-log database, petrophysical uncertainty can be addressed and stock-tank oil originally in place (STOOIP) can be calculated and mapped. Bakken Play The Williston basin oil province was discovered in 1951, and the Bakken formation was formally described in 1953. First oil was produced from the play in the same year. However, most of the early vertical wells, with the exception of those in the heavily fractured Antelope field, proved uneconomical to produce. The Bakken play is a hybrid of conventional (although tight) reservoirs and continuous resource plays that require development by use of closely spaced horizontal wells and multistage-hydraulic-fracture completions. Production Potential and Ranking Goals Regional ranking of future drilling locations is inherently challenging because of the many geological factors involved, and is complicated further by the influence of drilling, completion, and production operations. Traditional ranking schemes try to describe the relative potential of acreage and provide empirical support for the ranking by correlating the resulting rank to some measure of production performance. The production measure may be either a cumulative volume over a certain time frame (e.g., 90-day cumulative production) or a modeled estimated ultimate recovery (EUR) for the life of the well. Horizontal wells and multistage completions are a relatively new technology in the basin, with short production histories. As a result, use of actual production or actual production normalized per hydraulic-fracture stage is preferred because EUR introduces more assumptions and unknowns. A minimum period of 90 days of production was used in this study. Calibrating the regional-ranking predictions to actual production data will, inevitably, prove impossible to match past performance because the producing wells are subject to rapidly evolving technology and to variations in drilling, completion, production history, and operatorship.

An uncertainty modelling workflow for structurally compartmentalized reservoirs

Abstract Despite their significance, structural parameters are sometimes neglected in assessments of uncertainty on connected volumes and forecast production for compartmentalized reservoirs. A workflow is proposed for modelling multiple realizations of fault geometry and properties using 3D geomodelling software. Geometrical parameters that may be simulated include fault and horizon shape and location, fault displacement and fault pattern, while property variables include fault permeability, thickness and clay smears. Realizations are ranked by estimated connected volume, with selected models being exported for numerical flow simulation. Experimental design is used to assess sensitivity of forecast production and pressure to different parameters. The workflow is illustrated using a North Sea reservoir, in which structural heterogeneities cause considerable uncertainty on connected volumes, with implications for history matching and infill well planning. Fault geometry and permeability were the most important properties for all studied responses, however their relative significance could vary between early and late field life. A number of improvements are proposed, chiefly in the areas of connected volume estimation, handling of uncertain grid geometries and calculation of stress- or saturation-dependent fault permeabilities. Finally, the method can be integrated with conventional sedimentary and petrophysical uncertainties to investigate interactions and relative sensitivities with regard to structural parameters.

Static and Dynamic Uncertainty Management for Probabilistic Production Forecast in Chuchupa Field, Colombia

SummaryStructural, stratigraphic, and petrophysical uncertainties result in a wide range of geologic interpretations. For fields with a long production and pressure history, 3D dynamic simulations have been very useful in providing feedback to geologic modelers, which results in improved static models. For this study, we developed an integrated static and dynamic workflow to create a range of probabilistic simulation models to forecast dry-gas production under several production scenarios in the Chuchupa field.We selected eight geologic interpretations, representing the range of original gas in place (OGIP) and reservoir geometries determined in the static modeling, to perform dynamic history matches. The OGIP range of the models with very good history matches corresponds closely to the P10 to P90 OGIP range calculated from static modeling.In addition, we calibrated the various models with historical bottomhole and tubinghead flowing pressures and coupled the reservoir model with a network consisting of surface lines and equipment, pipelines from two platforms to the onshore sale-point station, and multistage compression to 1,215 psia. The set of probabilistic models is currently used to evaluate various production and market scenarios.