Technical Evaluation of Resource Plays

Arps in 1944 developed decline curve equations for analyzing reservoir/well production decline and the estimated decline rate constant is used for production forecast. In making reservoir production forecast, the Arps equations are usually calibrated to match the reservoir historical production decline by adjusting the decline rate constant value. The decline rate constant indirectly influences the reservoir properties responsible for production decline. However, the value assigned to the decline rate constant might either overestimate or underestimate the uncertainties in the reservoir rock and fluid properties responsible for reservoir production decline. The decline rate constant in the Arps equation has no equation relating it to the reservoir properties that are responsible for the reservoir production decline hence; the direction of uncertainty quantification on these reservoir parameters is not explicit, therefore undermining the predictive power of the Arps equations. The impact of reservoir properties uncertainties on the production rate decline and forecast by Arps equations cannot be examined due to the empirical nature of the Arps equations. Also, the decline rate constant is not explicitly related to reservoir rock and fluid properties responsible for production decline. However, the predictive power of the Arps model can be greatly improved if the decline constant can be expressed as a function of the reservoir rock and fluid properties responsible for production decline. Hence, the impact of uncertainty on the production forecast by Arps models can be examined. The objective of this work is to introduce reservoir rock and fluid properties into the Arps decline curve equations. This will enable the imposition of reservoir physics on the decline curve equations and the impact of uncertainties in these properties on matching historical production decline and forecast. In addition, a relationship between the decline rate constant in the Arps equations and the reservoir rock and fluid properties that influence reservoir production decline has been developed. A mechanistic approach using dimensional analysis was used to develop the relationship. The decline rate was assumed to be proportional to the various reservoir rock and fluid parameters, such as permeability, porosity, net-to-gross, drainage area, fluid density, and viscosity, that could influence production decline and by unit dimensional analysis, a relationship was developed between the decline constant and these reservoir rock and fluid properties. The decline rate constant was directly related to the product of reservoir permeability, fluid density, and the square of the pressure difference but inversely proportional to the cubic power of fluid viscosity. The relationship developed for the decline constant in this work will give the modified Arps model strong predictive power and allows for the impact of uncertainties on the production forecast to be evaluated. The results obtained from the modified Arps equations give better reservoir properties dependent outcomes than the traditional Arps equations and enable analysis of the effects of reservoir rock and fluid properties uncertainties on the production decline and forecast.

Summary The post-fracture pressure decay (PFPD) technique is a low-cost method allowing for stage-by-stage hydraulic fracture characterization. The analysis of the PFPD data is complex, with data affected by both hydraulic fracture and reservoir properties. Available analysis methods in the literature are oversimplified; reservoir or fracture properties are often assumed to be constant along the horizontal well, and therefore changes in the trend of pressure decay data are attributed to hydraulic fracture or reservoir properties only. Moreover, methods analogous to those applied to the analysis of conventional diagnostic fracture injection tests (DFITs) are often used and ignore critical mechanisms involved in main-stage hydraulic fracture stimulation. A conceptual numerical simulation study was first conducted herein to understand the key mechanisms involved in main-stage hydraulic fracturing. An analytical model was then developed to account for the dynamic behavior of the hydraulic fracture, leakoff, proppant distribution, multiple fractures, and propped- and unpropped-closure events. The analytical model is cast in the form of a new straightline analysis (SLA) method that provides stage-by-stage estimates of the ratio of unpropped fracture surface area to total fracture surface area. The SLA method was validated against numerical simulation results. Moreover, to account for the variation of reservoir properties along the horizontal well, the PFPD model is integrated with DFIT-flowback (DFIT-FBA) tests, performed at some points along the lateral, to obtain a reliable stage-by-stage hydraulic fracture and reservoir characterization approach. The practical application of the proposed integrated approach was demonstrated using PFPD and DFIT-FBA data from a horizontal well completed in 22 stages in the Montney Formation. The numerical simulation study demonstrated that the use of proppant and injection into multiple clusters (creating multiple fractures) results in multiple closure events. The closure process may start early after the pump-in period at a pressure significantly higher than the minimum in-situ stress. Using DFIT-based analytical models, which ignore the presence of proppant, causes significant errors in hydraulic fracture and reservoir property estimation. The PFPD field data examined herein exhibited a similar pressure trend to the numerical simulation cases. The ratio of unpropped fracture surface area to total fracture surface area was determined stage by stage using the PFPD SLA method, constrained by DFIT-FBA data. Engineers can use this information to optimize the hydraulic fracture stimulation design in real time, optimize the well spacing, and forecast the production. The cost and time advantages of this diagnostic method make this approach very attractive.

Triple-combo logs are important measurements for estimating geological, petrophysical, and geomechanical properties. Unfortunately, wireline and advanced LWD logs are typically dropped from the formation evaluation plan for unconventional wells due to economic constraints or borehole instability risks. Available measurements are typically measurement-while-drilling (MWD) natural gamma ray (GR) logs, along with surface measurements such as weight on bit (WOB), rate of penetration (ROP), torque, revolutions per minute (RPM), and differential pressure. The development of a robust and rapid model for predicting reservoir properties using this limited data set would be of high value for geological evaluation. Estimating such properties is a challenging task due to the nonlinear relationship between the available log data and unknown reservoir properties. A novel workflow is presented that combines two sequential models. First is a machine-learning algorithm to predict triple-combo logs from drilling dynamic measurements and GR logs. To train the machine-learning algorithm, well logs obtained from multiple wells located in the Eagle Ford and Permian Basins are scrutinized to identify important features. This process includes depth shifting, outlier detection, and feature selection, which allows for strategic hyperparameter tuning. Several regression algorithms are investigated, and it is found that gradient boosting algorithms yield superior prediction performance. Unlike commonly used regressors such as random forest methods, boosting algorithms train predictors sequentially, each trying to correct its predecessor. After triple-combo logs are predicted from measurement-while-drilling (MWD) logs, a physics-based joint inversion model is applied to estimate reservoir properties such as total porosity, clay volume, water saturation, volumetric concentrations of lithology, permeability, rock type, and geomechanical parameters. The trained model is deployed on a blind test well, and the predicted logs show excellent agreement when compared to the corresponding triple-combo measurements. The multimineral inversion using predicted triple-combo logs yields a geologic model that is validated with both mud logs and ECS measurements. Therefore, real-time estimated geological, petrophysical, and geomechanical properties can reveal complex geologic information to mitigate uncertainty related to drilling optimization, reservoir characterization, development plan, and reserve estimate. Using the MWD logs to predict triple-combo logs followed by a joint inversion is an innovative approach for a geological evaluation with a limited data set. The developed workflow can successfully provide (1) geologic lithofacies identification and rock typing, (2) more confidence in real-time drilling operation, (3) reservoir properties prediction, (4) missing log imputations and pseudo-log generation with forward modeling, (5) guidance for future logging and perforation (6) reference for seismic QI and well tie, and (7) computation time saving from days to minutes.

_ Since approximately 2012, large multistage completions in horizontal wells have significantly boosted oil production in the Bakken Formation. As of the time of writing, more than 18,000 stimulated wells have been completed in Bakken using various completion designs. Operators have experimented with well spacing, as well as the size and intensity of stimulation jobs by injecting increasingly large volumes of fluid and proppant to maximize oil production while minimizing costs. However, wells that received larger treatments did not always perform as expected. Furthermore, the aggregated impact of various interrelated completion design parameters and reservoir characteristics was not fully understood, leaving room for improvement in completion optimization evaluations. Publicly accessible completion and production data, meticulously collected by the North Dakota Industrial Commission (NDIC), combined with advancements in data science, have created an excellent opportunity to optimize drilling and completion strategies using statistical data analysis and predictive modeling. Observations and experiences from thousands of producing Bakken wells can now be analyzed and interpreted using data mining techniques. This article presents examples of data science applications in completion optimization calculations in Bakken production. Completion Design Optimization Using Predictive Modeling The initial optimization study analyzed drilling and completion results from more than 12,000 oil-producing wells across the Bakken in North Dakota using 2020 data (URTeC 3723843). Simple interpretations of the relationship between production performance and completion parameters, based on bivariate (two-dimensional) scatterplots, proved challenging due to the nonlinear nature of these dependencies. Data-mining techniques capable of accommodating nonlinear relationships and complex, incomplete information were applied to identify optimal completion practices. The dataset used in this analysis included eight publicly available completion design parameters: 1. Perforated interval 2. Injected fluid volume 3. Proppant amount 4. Stage count 5. Injection rate 6. Injection pressure 7. Proppant type 8. Completion type These parameters were used to predict well performance, measured by cumulative 6-month oil production. Predictive modeling was performed using the gradient boosting (GB) data-mining tool, in which multiple decision trees described the variability of the multidimensional dataset. The initial predictive modeling conducted for the entire Bakken play demonstrated acceptable performance for the training model (evaluated by R²), but the test model—using 20% of all data—showed weaker performance. This phenomenon, known as overfitting, occurred when a single statistical model struggled to accurately predict well production performance across various Bakken locations which are characterized by heterogeneous geology and reservoir properties.

This reference is for an abstract only. A full paper was not submitted for this conference. Abstract Description Reliability of long-term production forecasts issued from reservoir simulations is strongly dependent on the quality of reservoir models on which fluid flows simulations are run. In carbonate reservoirs, the usual difficulty for building reliable models is emphasized by the natural complexity of the reservoirs : biological aspects of sedimentation make very complex the reservoir architecture, which is usually overprint by early or late diagenesis and/or fracturing. High perm streaks, as well as cemented barriers, that control well productivity and stability of displacement front during production, are usually the result of such diagenetic overprint. Ignoring these geological features while constructing reservoir models may lead to deliver models unable to match actual production history and to carry out reliable production forecast. It is why, among all the challenging issues that exist in carbonate reservoir modelling domain, three of the main ones are related to :1. the ability to distribute in 3D, for the whole reservoir, the diagenetic features observed through well data,2. the management, during the modelling process, of multi-stages diagenesis which makes the reservoir architecture extremely complex with nested structures,3. the possibility to drive the reservoir modelling by results coming from dynamic data, in order to simplify the history matching stage during fluid flow reservoir simulation. This paper presents a new and very innovative methodology for meeting those three needs while modelling complex carbonate reservoirs affected by fluid-rock interactions. Application The methodology consists in a genetic-like simulation of the fluid-rock interaction and its effects on reservoir properties :1. Starting from a geological model in which all the heterogeneity from depositional pattern, early diagenesis, and fracturing is already input,2. A simulation is run to reproduce the evolution of the original reservoir properties during the diagenesis stage.3. The method consists in a 4D simulation, the reservoir being progressively modified by the circulation of mineralised or aggressive water.4. At any time-step of the simulation, the interaction between the fluid and the rock is represented through a mathematical function which modifies the probability for fluid displacement at the next time-step.5. The fluid displacement is driven by both reservoir properties at time of displacement, and by any gradient provided by the geological concept.6. Facies can react with fluid in a heterogeneous way, and fluid itself can lose or win power of agressivity on rock during the simulation.7. Nature of fluid-rock interaction, fluid gradient, or any geological parameter may change after a given stage of diagenesis for simulating a following stage.8. Objective functions are input for some of the reservoir properties, at local, regional, or field scale. Until target values are not reached, simulation goes on and the reservoir is modified. If dynamic properties are input in the objective function, the resulting model of the simulation should consist in an image of the reservoir easily history matched during fluid flow simulation. Results & Conclusions This new methodology was applied on a reservoir in which hydrothermal dolomite (HTD) has been identified. In that field, dolomitisation resulting from hydrothermal water circulation is systematically associated with faults and fractures. However, very thin layer can have been affected by fluids diverted in stratigraphic layers while moving up in the fault network. Effects on reservoir properties are locally considerable, reservoir permeability can be multiplied by 1000 after dolomite has replaced original limestone. Reservoir permeability at large scale is documented through well tests and interference tests. These data have helped to constrain the development of dolomite in the reservoir during the simulation. The evolution of the effects of fluid-rock interaction in the 4D space are really impressive and help to understand the extreme heterogeneity of reservoir properties. As a movie, the displacement of dolomitisation front in the fracture network, and the diversion in some stratigraphic layers, help geologists and reservoir engineers to identify zones with potentially high values of well productivity indexes, as well as zones of high risk for early and unexpected water breakthrough. Significance of Subject Matter The efficiency of this innovative approach has also been demonstrated in other types of fluid-rock interaction (meteoric dissolution, mixed water) and it offers a great interest for all carbonate reservoirs in which late diagenesis drives reservoir properties.

Variation in b-sigmoids with flow regime transitions in support of a new 3-segment DCA method: Improved production forecasting for tight oil and gas wells

The complexity of the micropore fabric and organic texture in tight unconventional reservoirs often complicates their correlations with physical rock property parameters. Anisotropy, which is a common physical property of tight oil reservoirs, can affect directional deformation and permeability during the reservoir development and pressure depletion period. Thus, accurately measuring seismic anisotropy and its relationships with petrophysical properties can refine our understanding of vertical transverse isotropy rocks and ultimately improve fracturing design as well as production forecasting. We have developed a measurement technique of ultrasonic velocities (P and S) from multiple directions, that is, bedding-parallel, bedding-perpendicular, and at 45° to bedding under simulated in situ reservoir pressure. Following these measurements, we also correlate these findings to pressure-dependent porosity and permeability, in addition to geologic attributes and textural anisotropy. The samples that we investigate are tight carbonaceous siltstone and organic-rich mudrock cores originating from the Bakken petroleum system, which include Lodgepole, upper Bakken shale, middle Bakken, lower Bakken shale, and Three Forks. Depending on the rock fabric and mineral constituents, each formation exhibits distinctive anisotropic behavior in response to pressure changes. The results indicate that the elastic parameters and petrophysical properties are inversely related under pressure for all samples. We also find that P-wave anisotropy and SH-wave anisotropy parameters decrease gradually with increasing confining pressure. The anisotropic behavior can be related to volumetric concentration and directional connectivity of clay layers, kerogen veins, and subparallel microfractures.

Well testing is an essential tool to estimate reserves and forecast production. The assessment depends on the analytical solution of the continuity and diffusivity equations which results in average reservoir properties. The key challenge is to acquire real-time data of pressure pulse signatures as they propagate and reach the boundaries. A potential solution is to use a permanent downhole pressure gauge or an array of distributed pressure sensors (DPS) placed at each hydraulic fracture cluster to characterize the flow. This work presents the elements of an innovative analytical model that uses this data to derive formation properties, visualize averaged flow dynamics, and evaluate the Stimulated Reservoir Volume (SRV). The real-time distributed pressure data, together with flow rate history, provides information that can be used to characterize the flow and estimate the boundary effect of hydraulic fractures and fissures. First, the numerically generated synthetic data is analyzed at each cluster to eliminate pressure drops due to friction. Next, analytical solutions of the continuity equation as well as trilinear models are used to invert reservoir properties to verify the proposed model. Based on the results, an advance statistical analysis is used to characterize the contribution of each variable to the flow rate. The numerical results suggest that there are key variables to identify different flow regimes. Numerical simulations are used to gauge the accuracy of the analytical model at predicting reservoir properties and flow patterns. Statistical analysis evinces that there are key parameters of the formation, fractures, and fissures that control the well productivity. The numerical analysis showed that for every reservoir type there are different combination of fracture parameters that can optimize the flow. Moreover, the results describe a method to obtain hydraulic fracture properties around each pressure sensor (DPS) and forecast their productivity. Finally, statistical learning was investigated as a potential solution to derive reservoir properties, including hydraulic and natural fractures, using the pressure pulse signature data without the need of inversion. The results show that there are key parameters that determine flow patterns. The importance of the accurate recognition and analysis of the multiple linear flow regimes at each cluster is in the potential to estimate the size of the SRV around hydraulic fractures during the transient life of the well. Moreover, this paper explains the procedure used for analyzing the change in the flow rate to obtain reservoir properties.

Excess natural gas produced in oilfields is routinely flared due to the absence of alternative uses. Similarly, landfills emit large quantities of methane, which is primarily flared or vented. Both these activities result in large scale energy waste and undesired methane and carbon dioxide emissions. This work examines the benefits of using excess natural gas to harvest atmospheric moisture. Natural gas-powered refrigeration systems can enable large scale dehumidification via condensation. The harvested water can be used for water-intensive operations like hydraulic fracturing, drilling and waterflooding in nearby oilfields. This solution thus addresses the issues of energy waste, water and greenhouse gas emissions A first-order model is used to estimate the water harvest, based on the gas flow rate, ambient weather and the refrigeration system. The benefits of flared gas-powered water harvesting are quantified for the Eagle Ford (Texas) and the Bakken (North Dakota) Shales, which account for the bulk of US flaring. The benefits of landfill gas-powered water harvesting are quantified for the Barnett (Texas), and Monterey (California) Shales, which can be served by 30 and 15 landfills, respectively. Overall, flared gas utilization for water production can meet 15% and 60% of the annual water requirements of the Eagle Ford and Bakken Shales, respectively. The water harvested using landfill gas (from nearby landfills) can meet 22% and 73% of the annual water requirements of the Barnett and Monterey Shales, respectively. This technology will also eliminate millions of trucking trips to transport water. Overall, this waste-to-value concept has global relevance, since a combination of excess gas availability, water scarcity and hot-humid conditions is common in many regions of the world.

This article, written by Technology Editor Dennis Denney, contains highlights of paper SPE 93554, "Using Integrated Project Models To Evaluate Field-Development Options," by M.G. Bilderbeck, SPE, and I.V. Beck, SPE, Gaffney, Cline & Assocs., prepared for the 2005 SPE Middle East Oil and Gas Show and Conference, Bahrain, 12-15 March. A practical and effective development-concept evaluation and screening methodology was developed that recognizes the fundamental relationship between reservoir potential and field-development strategy. The spreadsheet-based project-modeling environment integrates reservoir models and a field-development-planning model. The reservoir models are used to account for uncertainty in reservoir performance. The field-development-planning model handles the scheduling and cost estimation of wells and processing facilities. Introduction Decisions to invest in oilfield development are always made under conditions of uncertainty. The future performance of reservoirs is uncertain, as is the economic climate in which investment decisions are made and the resulting projects operated. Technical and commercial analyses used to evaluate the benefits of oilfield developments, and thereby support accompanying investment decision-making processes, must account for these uncertainties. Many types of uncertainty must be considered. Reservoir performance is affected primarily by the reservoir properties, which can never be defined fully at the point in time at which an investment decision is made. The cost of constructing and operating wells and production facilities also is uncertain, primarily because the design and use of these elements is a direct function of the performance of the reservoir. While the future performance of an oil field is uncertain, the reactions of managers to its performance are, by definition, deterministic. Managers comprehend the reality of a reservoir as it is revealed through its performance. Therefore, development plans must have sufficient flexibility to enable managers to react to, and accommodate, the performance of a reservoir with time. Integrated Evaluation Process The subsurface and surface elements of an oil field are linked and must be recognized and incorporated in any evaluation process used to support investment decisions. This process requires an appropriately staffed evaluation team and use of appropriate evaluation tools. A team with at least the following three broad technical disciplines should work together to perform an evaluation.

Production data analysis of unconventional gas wells: Review of theory and best practices

Summary Secondary oil recovery for the Jay/Little Escambia Creek (LEC) Fields Unit will exceed initial estimates by 27 × 10(6) bbl [4.3 × 10(6) m3] because of innovative reservoir management based on a comprehensive surveillance program and detailed reservoir description data. Infill program and detailed reservoir description data. Infill drill wells have accounted for 76 × 10(6) bbl [12 × 10(6) m3] of oil production. The mature waterflood was phased into a tertiary recovery project in 1981, and early phased into a tertiary recovery project in 1981, and early performance is generally consistent with the planning performance is generally consistent with the planning study, which predicted that 47 × 106 bbl [7.5 × 106 m3] of tertiary oil will be recovered. Introduction The Jay/LEC field waterflood was initiated in 1974. Waterflood performance has been extremely good, as indicated by the estimated primary plus secondary reserves of 346 × 10(6) bbl [55 × 10(6) m3] being surpassed in May 1983 with the field producing 40,000 B/D [6400 m3/d] of oil at the time. Ultimate recovery from primary and secondary is now expected to be 373 × 10(6) STB [59 × 10(6) stock-tank m3] or 51 % of the original oil in place (OOIP). The major contributing factor to this success was effective reservoir management, which originated from results of reservoir description and surveillance programs. Following 8 years' technical evaluation, a fieldwide miscible gas displacement project began in Jan. 1981 using available field methane gas. High-pressure N2 injection began in Dec. 1981 when this cheaper gas and high-pressure compression facilities became available. By mid-1984, 2.9% hydrocarbon pore volume (HCPV) of the 20% HCPV design of miscible gas had been injected. Current injection operations use a total of 61 × 10(6) cu ft [1.7 × 10(6) m3] N2 and 172,000 B/D [27 000 m3/d] water in a water-alternating-gas (WAG) scheme. As expected, water injectivity has decreased significantly but presents no serious operational problem. A total of 29 spinner surveys have been conducted problem. A total of 29 spinner surveys have been conducted in 18 injection wellsto define the initial gas profiles andto determine the effects of injection pressure and duration of the gas injection cycle on the profiles. Initial nitrogen breakthrough occurred in Dec. 1982 and 14 of the 67 currently producing wells have experienced nitrogen production. Oil production response from these wells has been identifiable but varies significantly between wells. This paper discusses the performance of the highly successful waterflood and describes how, in the later life of the flood, it was phased into the tertiary oil recovery project. project. Reservoir and Fluid Properties The Jay/LEC field is located in the Florida panhandle and south Alabama. The oil accumulation is in the Smackover carbonate and Norphlet sand formations. Oil occurs mostly in the dolomitized portions of the Smackover. The formation is found below 15,000 ft [4572 m] and has an average thickness of about 350 ft [107 m]. All wells drilled in the field, including the infill wells, were cored conventionally through the Smackover formation. Original reservoir pressure was 7,850 psi [54 MPa] about 5,000 psi [35 MPa] above the saturation pressure of 2,830 psi [20 MPa]. No free hydrocarbon gas pressure of 2,830 psi [20 MPa]. No free hydrocarbon gas has been released from the reservoir oil, since minimum pressure was 4,800 psi [33 MPa]. Table 1 presents a pressure was 4,800 psi [33 MPa]. Table 1 presents a summary of rock, fluid, and reservoir properties. Waterflood Performance Following unitization and flood start in March 1974 a comprehensive surveillance program was implemented. Surveillance data were combined with the abundance of reservoir description information to provide a highly successful reservoir management program for this complex carbonate waterflood. Surveillance data were obtained from several production logging tools to determine initial and subsequent vertical flood conformance. Additionally, radioactive tracers, reservoir pressure surveys, and routine well test data were used to define areal conformance. Several programs to improve recovery evolved from this effort. Surveillance techniques and resulting programs are treated in more detail in Refs. 1 and 3. The programs are treated in more detail in Refs. 1 and 3. The infill drilling program, which began in 1977, is particularly significant. To mid-1984, a total of 37 infill particularly significant. To mid-1984, a total of 37 infill wells were drilled, and the average well spacing was reduced from 160 acres [647 × 10(3) m] per well to 112 acres [453 × 10(3) m]. Table 2 presents the updated results of this program. Infill wells have accounted for 76 ⨯ 10(6) bbl [12 × 10(6) m3] of oil production and currently are producing 17,200 B/D [2700 m3/d] oil. JPT P. 261

The Bakken formation is located in parts of Montana, North Dakota, and Saskatchewan, Cananda. North Dakota alone produces more than 700,000 barrels per day and still growing, which accounts for 11 percent of the domestic total crude oil production, while the oil recovery factor of 1% in Bakken is necessary to be improved to a higher level. Current practice involves fracture stimulation of the mature Upper and Lower Bakken Shales above and below the middle layer (pay zone in carbonate or siltstone). The fractures penetrate and connect natural fractures within the reservoirs and promote more efficient drainage of the oil. This situation suggests that a surfactant technology incorporating in the well stimulation fluids at low dosage can be a fit in Bakken formation to enhance oil recovery. If properly designed, such additives in the fracture fluids will penetrate into the highly oil-saturated matrix or natural fracture region and accelerate the extraction of the oil in place by rapid imbibition. Extracted oil can readily flow from the matrix into the propped fracture system. Another benefit of this surfactant technology is its engineered property to leave the matrix or natural fracture face water-wet to facilitate oil movement during production. This paper presents a study of a series of such stimulation fluid additives developed for enhanced oil recovery. Crude oil samples from the Bakken formation were analyzed for several properties pertinent to surfactant formulation including organic constituents, total acid number, and viscosity. Compatibility tests were carried out including surfactant/produced water interaction, emulsion tendency, and surfactant compatibility with proposed fracturing fluids. A spontaneous imbibition process was employed to mimic surfactant additives penetrating the tight matrix and microfractures to recover more hydrocarbons. The results show that more than one of these products improved recovery of Bakken crude oil by spontaneous imbibition. The best of these products is recommended for field application.

While the popularity of the numerical simulation approach (such as the Monte Carlo) for probabilistic reserves estimation has never been higher among geoscientists and reservoir engineers, comparatively little attention has been paid to the techniques needed to correctly model the required petrophysical input parameters. Petrophysical well analyses typically end in the presentation of average reservoir property values for porosity, water saturation and net-to-gross but these lone averages, while adequate for deterministic reserves estimates, obviously fail to account for the inherent reservoir petrophysical properties uncertainty within a probabilistic reserves estimate. Of more importance for probabilistic reserves estimation is a statistical model comprising Probability Distribution Functions (or PDFs) of the aerial uncertainty in reservoir properties resulting from a larger reservoir being sampled by a relatively small discrete well population. Monte Carlo simulations are typically used to estimate probabilistic reserves but the accuracy of these are strongly correlated to the quality of the input probability distributions. Many errors in the outputs of reserves models could be traced back to assigning incorrect values to the descriptive parameters of a distribution, and indeed the selection of an appropriate distribution. Therefore, if probability distributions are to be used to represent statistical assumptions of petrophysical properties in reserves estimates, then the way petrophysical averages data is analysed and transformed into PDFs becomes of crucial importance. Hence, the purpose of this paper is to present practical ideas for achieving the definition of probability distributions that best represent the petrophysical property input into probabilistic reserves estimates. The focus on petrophysical properties in this paper is not arbitrary or accidental but, instead, reflects the centrality of petrophysical input as a driver of accuracy in reserves estimates.

Identifying oil-saturated versus water-saturated sands in shallow, unconsolidated, viscous-oil-bearing terrigenous-clastic reservoirs of Kuwait field is challenging. Field appraisal was based upon seismic, core and wireline-log data from 19 wells. Static and dynamic models incorporating all subsurface data were built to estimate oil-in-place and forecast production. Estimating and modeling fluid saturations in reservoir zones was accomplished by integrating core, dielectric-resistivity, Nuclear Magnetic Resonance (NMR) and Wireline Formation Tester (WFT) data. Wells were drilled along a northwest/southeast-trend, thus geologic and reservoir-property variability in east and west parts of the field are uncertain. Stratigraphy and lithologic properties in these Miocene-age fluvial to shallow-marine strata impart a complex 3–D fluid distribution in the field. Repeated shoreline progradations and retrogradations deposited a stratigraphic succession defined by five facies-associations (i.e., shoal, tidal flat, tidal channel, lagoon, sabkha). Five lithofacies (i.e., shale, shaly sandstone, sandstone, carbonate-cemented sandstone, evaporite) were identified from core, elemental spectroscopy logs, and X-ray diffraction (XRD) data. Facies associations and lithofacies models were built using a combination of multiple-point statistics and sequential-indicator simulation. Lithofacies distribution in the static model was constrained by the facies-association distribution; reservoir-property distribution (e.g., porosity, permeability) was conditioned by lithofacies. Discrete reservoir zones were defined to separate oil-saturated versus water-saturated sands. The volume and position of oil-bearing sands are controlled by the defined zones and permeability distribution. The oil-filling process in these viscous oil-bearing reservoirs is typically controlled by the pore throat distribution with the migrating oil taking the path of least resistance. Due to the presence of stratigraphic-flow baffles, fluid contacts vary from sand-to-sand vertically and laterally. Log data, core descriptions, ultraviolet photographs, WFT and pressure volume temperature (PVT) data guided the interpretation of lowest known oil and highest known water levels, thus reducing fluid saturation uncertainty in the field.