Field Development Strategy Research Articles

4D Seismic With Reservoir Simulation Improves Reservoir Forecasting

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 191521, “Quantitative Integration of 4D Seismic with Reservoir Simulation,” by Sarath Pavan Ketineni, SPE, Subhash Kalla, SPE, Shauna Oppert, SPE, and Travis Billiter, SPE, Chevron, prepared for the 2018 SPE Annual Technical Conference and Exhibition, Dallas, 24–26 September. The paper has not been peer reviewed. Standard history-matching work flows use qualitative 4D seismic observations in reservoir modeling and simulation. However, such work flows lack a robust framework for quantitatively integrating 4D seismic interpretations. Four-dimensional, or time-lapse, seismic interpretations provide valuable interwell saturation and pressure information. Quantitatively integrating these data can help constrain simulation parameters and improve the reliability of production modeling. This paper outlines the value of 4D for reducing uncertainty in the range of history-matched models and improving the production forecast. Introduction Reservoir-simulation models play an essential role in generating optimal field-development strategies, but they need to be history-matched before they can be used for reliable forecasting. Traditional history matching of a reservoir involves matching observed production and pressure data at well locations by changing the uncertain parameters in the reservoir model within the acceptable range. The parameters can be classified broadly as static and dynamic. Static parameters include permeability, porosity, and net to gross, among many others. Dynamic parameters may include oil/water contacts, fault transmissibilities, relative permeability curves, and flow pathways. Deterministic modeling focuses on a single scenario of the reservoir model, ignoring the effect of uncertainty in the parameters considered. Probabilistic history matching is the process of selecting non unique and multiple history-matched reservoir models by altering the uncertain static and dynamic parameters to obtain a range of possible forecasts. Typically, in probabilistic work flows, objective functions composed of a combination of differences in measured and simulated bottomhole pressure data and individual fluid-phase flow rates are used to identify significant factors or uncertainties. The functions are typically based on surveillance data obtained at well locations and therefore may not be sensitive to key reservoir uncertainty away from wells. A good match with production data for a given reservoir model does not ensure robustness in making performance forecasts. Reservoir heterogeneities—high-permeability pathways, barriers and baffles, or vertical connections forged from geologic erosion—can significantly affect drainage and swept patterns and well-production forecasts. Especially during early stages of field production, history matching to pressure and fluid-rate production data often lacks information necessary to resolve these critical heterogeneities fully, which may significantly affect production. Even a well-posed probabilistic history-matching approach cannot incorporate the variety and complexities of heterogeneities that are yet to have a strong imprint on future production because of insufficient data away from wells and the sheer number of static and dynamic uncertainties that can affect production.

Environmental and Operational Performance of CO2-EOR as a CCUS Technology: A Cranfield Example with Dynamic LCA Considerations

This study evaluates the potential of carbon dioxide-enhanced oil recovery (CO2-EOR) to reduce greenhouse gas emissions without compromising oil production goals. A novel, dynamic carbon lifecycle analysis (d-LCA) was developed and used to understand the evolution of the environmental impact (CO2 emissions) and mitigation (geologic CO2 storage) associated with an expanded carbon capture, utilization and storage (CCUS) system, from start to closure of operations. EOR operational performance was assessed through CO2 utilization rates, which relate usage of CO2 to oil production. Because field operational strategies have a significant impact on reservoir engineering parameters that affect both CO2 storage and oil production (e.g., sweep efficiency, flood conformance, fluid saturation distribution), we conducted a scenario analysis that assessed the operational and environmental performance of four common and novel CO2-EOR field development strategies. Each scenario was evaluated with and without stacked saline carbon storage, an EOR/storage combination strategy where excess CO2 from the recycling facility is injected into an underlying saline aquifer for long-term carbon storage. The dynamic interplay between operational and environmental performance formed the basis of our CCUS technology analysis. The results showed that all CO2-EOR evaluated scenarios start operating with a negative carbon footprint and, years into the project, transitioned into operating with a positive carbon footprint. The transition points were significantly different in each scenario. Water-alternating-gas (WAG) was identified as the CO2 injection strategy with the highest potential to co-optimize EOR and carbon storage goals. The results provide an understanding of the evolution of the system’s net carbon balance in all four field development strategies studied. The environmental performance can be significantly improved with stacked storage, where a negative carbon footprint can be maintained throughout the life of the operation in most of the injection scenarios modelled. This information will be useful to CO2-EOR operators seeking value in storing more CO2 through a carbon credit program (e.g., the 45Q carbon credit program in the USA). Most importantly, this study serves as confirmation that CO2-EOR can be operationally designed to both enhance oil production and reduce greenhouse gas emissions into the atmosphere.

A Comparative Study of CO2-Flood Displacement Efficiency for Different CO2 Injection Strategies: Permian Basin vs. U.S. Gulf Coast

In CO2-EOR, there are two main field development strategies: water-alternating-gas (WAG) and continuous gas injection (CGI). The aim of our study is to compare these strategies in terms of their economic performance (from the basis of incremental oil recovery) and in terms of their environmental performance (from the basis of ultimate CO2 storage volumes). Within this framework and to demonstrate the efficiency of each strategy, we evaluate the distribution of Carbon Dioxide in oil, gas, and brine phases; the amount of total CO2 stored at the end of the project; the incremental oil recovery; and the CO2 utilization ratios. In this study, we model and compare two fields which represent two different reservoir settings: Cranfield (representative of the U.S. Gulf Coast sandstone reservoirs) and SACROC (representative of the Permian Basin carbonate reservoirs). CGI is the original operating strategy in Cranfield and WAG is the original operating strategy applied in the SACROC unit. High resolution geocellular models are used for both Cranfield and SACROC fields. The models are constructed based on wire-line logs, seismic surveys, core data, and stratigraphic interpretations. A comprehensive pressure-production history matching for primary, secondary, and tertiary recovery is conducted for both of the fields. Different operating strategies are designed for each field (e.g. CGI and WAG). After finishing the history-matching, CGI and WAG scenarios are simulated in both models to compare their performances. CO2 partitioning in oil, brine, and gas phase (mobile or residual) are compared for both scenarios (WAG and CGI). The partitioning of CO2 in oil results in CO2 miscibility in oil, the partitioning of CO2 in brine results in CO2 dissolution in brine, and CO2 partitioning in gas phase are divided into structural trapping and residual trapping of CO2. We plotted the contribution of different trapping mechanisms over a post injection period for WAG and CGI for both SACROC and Cranfield. Additionally, the total CO2 storage, the incremental oil recovery, and CO2 utilization ratios are compared in both scenarios for both fields. Although actual operating strategy in these two fields are different (CGI in Cranfield and WAG in SACROC), our numerical modelling results show that WAG could not only balance the CO2 storage, incremental oil recovery, and CO2 utilization ratio but also store the trapped CO2 with lower risk of leakage in both fields (by decreasing the amount of structurally trapped CO2). Because of multiple alternation of CO2 and water slugs in WAG, this approach reduces the viscous instability and therefore the efficiency of oil recovery. Our study shows that the distribution of CO2 in different phases is different for each field. Because of the lower minimum miscibility pressure (MMP) and lighter initial oil saturation in SACROC, the partitioning of CO2 in oil is much higher in SACROC than in Cranfield. The dissolution of CO2 in brine is much higher in Cranfield because of the presence of strong aquifer near injection wells. The present work provides valuable insights for optimizing oil production and CO2 storage in a CO2-EOR project. Additionally, this study clearly shows the impact of development strategies on the relative importance of different trapping mechanisms.

Geological Life Cycle Inventory Model Development for Shale Gas Resources

This paper presents an innovative life cycle assessment approach for shale gas production, which integrates geological system characteristics with a surface emission model to estimate the overall GHG emissions. Firstly, the model developed incorporates a Volumetric Estimation technique, Flowing Material Balance and Advance Decline Curve analysis to estimate the field gas in-place and estimated ultimate resource (EUR). Next, this geological model is integrated with the surface emission model, which is based on engineering design and operational parameters, to estimate the GHG as well as other emissions (direct and indirect; gaseous, liquid, solid waste) and resource use (materials, energy, water) from site construction, drilling, hydraulic fracturing, well completion, well head operations, gas gathering and gas processing. The cradle to gate LCA model developed was applied to the Fayetteville shale gas field using public domain data. Three possible field development strategies were developed and implemented in the life cycle inventory model developed. They all comply with the outputs of the subsurface constraint inventory model but differ in terms of selected parameters in well construction, well drilling, hydraulic fracturing, well completion, well head and surface facilities operations. The annual GHG emissions of Fayetteville shale gas field production with a sensitivity analysis was estimated for the three different field development strategies. The resulting life cycle GHG emissions range between 12.79 and 21.09 CO2 equivalents per Mscf of gas produced or 13.65 to 22.51 CO2 equivalents per MJ of gas produced. The water consumption is estimated in the range of 18 litres of water per Mscf of gas production.

Effect of mobility and convection-dominated flow on evaluation of reservoir dynamic performance by fast marching method

The determination of optimum well locations and number of wells needed during green field development always comes with unprecedented challenges because of the geological uncertainty, and the non-linear relationship between the input and output variables associated with real reservoirs. These variables are key sources affecting the viability and validity of the results. Reservoir simulation is one of the least uncertain and most reliable prediction tools for dynamic performance of any reservoir. As field development progresses, more information becomes available, enabling us to continually update and, if needed, correct the reservoir description. The simulator can then be used to perform a variety of exercises or scenarios, with the goal of optimizing field development and operation strategies. Optimizing well numbers or locations under such geological uncertainty is achieved by using a reservoir simulator under several geological realizations, and these require multiple reservoir simulations to estimate the field performance for a given well configuration at a given location. Using reservoir simulation becomes impractical when dealing with real field cases incorporating multi-million cells because of the associated CPU demand constraints (Bouzarkouna et al. 2011). For instance, to determine the optimum well locations in a giant field that will result in the most efficient production rate scenario, one requires a large number of simulation runs for different realizations and well configurations. A large amount of runs is technically difficult to achieve even if we have access to super computers. The fast marching method (FMM), which is based on a solution of Eikonal equation, can be used to find the optimum well locations in a green oil field by tracking the pressure distribution in the reservoir. The FMM will enable us to calculate the radius of investigation or pressure front as a function of time without running any simulation and with a high degree of accuracy under primary depletion conditions. The main purpose of this paper is to study the effect of mobility on FMM and extend the investigation of its validity to include two-phase flow and convection-dominated flow and evaluate the ability of the methodology to predict the dynamic performance of the reservoir during pseudo-steady-state flow regime.

Impact of field development strategies on CO2 trapping mechanisms in a CO2–EOR field: A case study in the permian basin (SACROC unit)

This paper presents field-scale compositional reservoir flow modeling in the SACROC (Scurry Area Canyon Reef Operators Committee) unit, Permian Basin, to demonstrate the relative partitioning of CO2 during and after CO2 injection. The model was developed to study the effect of structural trapping, solubility trapping, and residual trapping on the partitioning of CO2 in oil, gas (free or residual), and brine phases over time. Furthermore, we investigated the impact of various injection scenarios, such as CGI (Continuous Gas Injection) and WAG (Water Alternating Gas), on the different trapping mechanisms. First, we used a high-resolution geocellular model, which was constructed from wireline logs, seismic surveys, core data, and stratigraphic interpretation. As the initial distribution of fluids plays an important role in CO2 partitioning, a comprehensive pressure-production history matching of primary, secondary, and tertiary recovery was completed. The hysteresis model was used to calculate the amount of CO2 trapped as residual. CO2 solubility into brine was verified based on previous experiments. The model results show a new understanding of relative CO2 partitioning in porous media. Although it was believed that structural trapping is the largest of the trapping mechanisms during CO2 injection and the first years of post-inject ion, our results show that in a carbonate field like SACROC, which contains a light oil, the solubility of CO2 in oil plays a very important role, even in the first stage of CO2 injection. Running a comprehensive history matching shows the deficiency of previous models to estimate the amount of trapped CO2 during and after the injection period. Among the various scenarios explored, WAG seems to be a promising operational approach to balance both storage and oil production. The present work provides valuable insights for optimizing oil production and CO2 storage in carbonate reservoirs like SACROC unit. In addition, this work helps decision makers to set storage goals based on optimized project risks.

ANALISIS POTENSI LAHAN DAN STRATEGI PENGEMBANGAN SAWAH BARU SECARA BERKELANJUTAN DI KECAMATAN JATIGEDE, KABUPATEN SUMEDANG, PROVINSI JAWA BARAT

The landuse change from paddy field to non-rice field in Java is increasing every year and this has an impact on the decreasing of national food security. Likewise the occurrence of land conversion of around 2,000 ha for the construction of Jatigede reservoir in Sumedang Regency, West Java Province has an impact on the decrease of rice production about 24 thousand tons of GKG (assuming rice productivity 6 ton/ha). The development of new sustainable rice fields is one of the solutions to improve food security and sustainability in the future both in Sumedang and Indonesia. The objectives of this research are: 1) to analyze the availability and suitability of land for the development of new paddy fields; 2) to analyze environmental feasibility and cost (environmental cost) in new rice field development activities; 3) analyze farmer institutional support in new rice field development activities; 4) to formulate the direction of new rice field development plan; and 5) to formulate a new rice field development strategy in Jatigede sub-district. The results are expected to be input for future planning for the development of new rice fields in Jatigede Subdistrict, Sumedang Regency. The study was conducted from December 2016 to May 2017. The type of data used consists of primary data and secondary data. Primary data is obtained from direct surveys to the field by conducting ground checks or through interviews. The results showed that the potential land available for the development of new paddy fields in Jatigede sub-district was 3,419 ha and from the potential land area will be developed into new paddy fields of 346 ha (4.18%) spread over 4 (four) villages, ie Cipicung 54 ha, Ciranggem Village covering 94 ha, Cisampih Village covering 107 ha and Karedok Village area of 91 ha. The result of feasibility test on new paddy field development activity in Jatigede subdistrict shows Net Present Value (NPV) is Rp. 92,893,000,000 and Benefit Cost Ratio (B/C) 3.32. Development strategies are: Respectively development strategies comprise (1) Optimize government financing assistance to accelerate the implementation of new paddy field target, (2) Together with the head of the farmer group association assist the paddy field construction process, (3) Conduct a survey of water resources and other supporting infrastructure around paddy field area, (4) Ensuring the new paddy field is not overlapping with other area, (5) Socialize the long-term benefits of the development of new paddy field by getting subsidy for three year since the first year development. (6) Encourage local governments to established immediately as LP2B areas and included in the RTRW of Jatigede District.

An experimental study of the low salinity Smart Water - Polymer hybrid EOR effect in sandstone material

Previously reported experimental work has shown positive oil recovery effects by combining low salinity brine with polymer for injection into sandstone cores. The extra oil recovered by the combination of the two EOR methods was termed a “Hybrid EOR effect”.The experimental work performed in the present study assessed the potential of combining low salinity (LS) Smart Water injection with polymer (P) flooding; LSP flooding. Dramatic improvements in the EOR effect were observed when combining LS in secondary mode with polymer injection in tertiary mode.Secondary LS injection gave 59 %OOIP after 1 PV injected, which is an outstanding result compared to secondary formation water (FW) injection reaching only 35 %OOIP after 1 PV, and an ultimate recovery of 40 %OOIP after 2.5 PV. The ultimate recovery with seawater (SW) in secondary mode was 44% OOIP, showing only a slightly better performance than FW, but poorer efficiency than the ultimate LS brine recovery of 64 %OOIP.In comparison, the ultimate recovery with secondary LSP injection was 66 %OOIP. The displacement front was stable and fast, but it did not give a substantial increase in oil recovery in comparison to the secondary LS injection. Tertiary LSP injection after secondary LS injection gave the highest oil recoveries with a total hybrid EOR effect of 86 %OOIP. All tertiary LSP floods gave improved displacement stability with EOR effects of ∼20 %OOIP. The positive EOR effects are a result of redistribution of oil within the pore space after wettability alteration with the Smart Water. Oil attached to the mineral surface has low mobility, but at more water-wet conditions a new fraction of mobile oil, which is more easily mobilized with a polymer solution, is created (Wang et al., 2001).The combination of Smart Water with polymer flooding appears to have a huge potential for future EOR applications. Field development strategies for new sandstone reservoirs should include wettability alteration and redistribution of oil within the pores by Smart Water and improved displacement efficiency with polymer for optimized EOR to lowest cost.

Analysis of CO2 storage mechanisms at a CO2‐EOR site, Cranfield, Mississippi

AbstractCarbon dioxide injected for enhanced oil recovery will partition into several phases in the target geological formation. The distribution into free or residually trapped oil, gas, and brine phases depends on many factors such as reservoir temperature and pressure, initial fluid saturations, brine salinity, and relative permeability parameters, and evolves through time including in the post‐injection period, during which it will tend to stabilize. Our numerical simulations, based on Cranfield, MS CO2‐EOR project data, demonstrate that these variations are significant and mostly dependent on the operator's selected field‐development strategy: continuous gas (CO2) injection, water alternating gas (WAG) injection, water curtain injection, or combinations thereof. In summary, our work shows that the field development strategy selected by the operator has a major impact on the relative importance of the different trapping mechanisms and that WAG seems to be a promising operational approach to balance both CO2 storage and oil production. © 2018 Society of Chemical Industry and John Wiley & Sons, Ltd.

Feasibility assessment of heavy-oil recovery by CO2 injection after cold production with sands: Lab-to-field scale modeling considering non-equilibrium foamy oil behavior

With notion to the potential use of cyclical CO2 injection as a follow-up post-CHOPS (Cold Heavy Oil Production with Sand) EOR option, a comprehensive core-to-field scale study was performed to investigate the non-equilibrium CO2 behavior. PVT lab measurements of both CO2 injection and depletion tests were performed and modeled under highly controlled conditions and at different temperatures to obtain equilibrium properties. To address the kinetics of the non-equilibrium process, a 1.5-m multiple pressure-port core holder was designed to measure the differential pressure in the various sections of the sand-pack during CO2 depletion tests. Oil and gas volumes and the signature of pressures along the core holder length were recorded at each trial.Next, the CO2 depletion experiments were numerically simulated and the parameters of reaction kinetics used to model the liberation of dissolved CO2 gas to trapped CO2 bubbles and then to free CO2 gas non-equilibrium foamy oil depletion) were determined. The grid sensitivity of the reaction kinetics was investigated and a power-law core-to-field scaling rule was developed and numerically validated. The scaling rule was then used to upscale the reaction parameters for a previously history matched CHOPS field with 15 wells in Alberta (Rangriz Shokri and Babadagli, 2012, 2014). Lastly, the economic feasibility assessment of cyclic CO2 stimulation as a follow-up field-scale post-CHOPS option was determined under different oil price scenarios to optimize the operating conditions and the selection of the best possible field development strategies.

Streamline-based history matching of bottomhole pressure and three-phase production data using a multiscale approach

Reconciling geological models to the available dynamic information, commonly known as history matching, is an essential step for optimizing reservoir management and field development strategies, including improved recovery methods. There are several challenges in the current history matching workflow, particularly for high resolution geologic models with multimillion cells and complex geologic architecture. Streamline-based inverse modeling has shown great promise in this respect because of computational efficiency and analytic calculation of sensitivity of production response to reservoir properties. However, the current streamline-based approach has mostly been applied to history matching water-cut and tracer response in two-phase flow.More recently streamline-based sensitivity calculations have been extended to bottomhole pressure and gas-oil ratio. This allows for generalization of streamline-based history matching to the three-phase. In this paper we present an integrated application of the streamline-based three-phase history matching by incorporating water-cut, gas-oil ratio and bottomhole pressure data while updating high resolution geologic models. The crux of our approach lies in the analytic computation of well response sensitivities based on streamline which allows for efficient inversion of production and pressure data. We also validate the accuracy and efficiency of the streamline-based bottomhole pressure sensitivities by comparison with an adjoint-based method using a finite-difference simulator. In the history matching workflow, we incorporate the streamline-based method within a multiscale framework to account for the disparity in resolution of different types of history data. This integrated method proposed in this paper leads to capturing of the large- and fine-scale heterogeneity while matching the pressure and production responses efficiently.We demonstrate the power and utility of the streamline-based approach using synthetic and field applications with three phases. The synthetic example involves the SPE9 benchmark field case with waterflooding and aquifer drive. The field example involves full-field history matching of the Norne Field in the North Sea using water-cut, gas-oil ratio and bottomhole pressure data. The proposed multiscale workflow clearly demonstrates the power and advantage of our approach in achieving geologically consistent history matching at the full-field level.

Drill and Learn: A Decision-Making Work Flow To Quantify Value of Learning

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 182719, “Drill and Learn: A Decision-Making Work Flow To Quantify Value of Learning,” by R.G. Hanea, SPE, P. Casanova, and L. Hustoft, Statoil; R.B. Bratvold, SPE, University of Stavanger; and R. Nair, C. Hewson, SPE, O. Leeuwenburgh, and R.M. Fonseca, TNO, prepared for the 2017 SPE Reservoir Simulation Conference, Montgomery, Texas, USA, 20–22 February. The paper has not been peer reviewed. Uncertainty assessment and reduction are often elements of high-quality decision making, although they are not, in themselves, value creating. Value can be created only through decisions, and any decision changes resulting from assisted history matching should be modeled explicitly. This paper presents a comparison of existing work flows and introduces a practically driven approach, referred to as “drill and learn,” using elements and concepts from existing work flows to quantify the value of learning (VOL). Introduction The idea to apply numerical optimization methods to reservoir models in order to arrive at optimal field-development plans has been around for a long time. Early methods for optimization were quite limiting, however, in terms of the complexity of the problems that could be addressed. Recent developments in algorithms and computing power have made it possible to begin to address the full complexity of the field-development optimization problem, including a large number of decision variables of various types, a better characterization of geological uncertainty, handling of realistic platform constraints, and operating strategies of newly drilled wells. Closed-loop reservoir management aims to incorporate new information into models and optimize field-development or reservoir-management strategies on a nearly continuous basis. The main assumption underlying the closed-loop-reservoir-management framework is that acquiring information can change decisions about how the field should be developed or operated such that certain performance objectives are improved. This assumption is identical to that underlying the concept of value-of-information (VOI) determination, which addresses the question of whether one should actually acquire specific data considering not only the expected effect on the system performance but also the cost of acquiring these data from which the information is to be extracted. A first attempt to investigate the feasibility of applying a decision analytic VOI work flow to quantitative reservoir-model-based decision making tried to evaluate the increase in expected economic value with and without the use of specific data with associated measurement errors. The probabilistic aspect of a VOI evaluation was accounted for by the use of an ensemble of models that capture geological uncertainty, and the value was based on applying optimization methods to the model ensemble and evaluating the resulting strategies on synthetic truth models that were not part of the ensemble. Improvements to the computational complexity of the originally proposed work flow were suggested in subsequent work. Still, even with the suggested modifications, the computational cost of a formal VOI evaluation would remain prohibitive for many real-field models without further modification or simplification.

Improved decision making with new efficient workflows for well placement optimization

Determining optimum infill well placement has been one of most challenging events in overall field development strategy of any types of field. Because there could be a large number of possible candidates for new infill well locations, it is not practically feasible to evaluate all candidate locations, particularly at high-resolution geological models including millions of geological cells. Conventional static measure maps generally used in the industry has limited applicability as this does not address dynamic fluid flow and interference between existing wells. In contrast, direct application of stochastic search optimization methods such as genetic algorithms to large-scale field models may better account for dynamically changing reservoir conditions but can be complex to apply or computationally expensive.We propose a novel method for infill well placement optimization designed as a two-stage optimization based on both dynamic flow diagnostic characteristics and genetic algorithm optimization methods. The main advantage of the proposed method over the conventional approach of using static reservoir property maps is its ability to integrate static reservoir properties with dynamic variables such as sweep efficiencies. By incorporating sweep efficiencies, new infill well potential map is generated to guide the optimization. The other benefits of this method are its maneuverability and capability to search in a narrower and tight space making the evolutionary algorithm an appropriate choice to identify rewarding locations which was not practically applicable due to the significant computational burden.The proposed automated workflow is seamlessly integrated from building a simulation model to performing reservoir simulation and conducting the optimization process. The workflow is combined with novel engineering methods to identify best rewarding well location areas, and to identify strategies to maximize sweep efficiency through optimizing well placement and completion strategies resulting in substantial engineering time-savings. The power and utility of the proposed workflow are first demonstrated by a synthetic example and then applied to a full field model. Strategies to improve incremental oil recoveries were identified with a proposed novel, iterative and robust workflow.

NEW APPROACH TO CRUDE OIL CLASSIFICATION PROBLEM

Formulation of the problem. The problem of crude oil classification has existed since the beginning of oil production. Presently, there is a lot of available information about chemical composition and physical-chemical properties of crude oil found in various petroleum- bearing regions around the world, however, the problem with oil typification still exists and there is no uniform classification. Primarily, this is connected with significant differences in chemical composition of oil and absence of a proven set of key criteria that such classification can be based on. The available classifications can be relatively divided into three categories: chemical, genetic and process. However, there is no classification that would approach oil regarding its production problems. When choosing a recovery technique and development strategy for a particular oil deposit, one has to refer to several classifications simultaneously and, in some cases, one classification may contradict another. The aim of the investigation. To develop a new industrial crude oil classification. Methods. In order to determine the key factors affecting physical and chemical properties of oil, 150 oil reservoirs of Dnieper-Donets basin containing oil with various composition have been studied using well operation and testing data. Results. Obtained results have confirmed the relationship between the three key oil content components - asphaltenes and waxes, sulfur and resins, which affect specific physical-chemical properties of oil. None of the existing classifications account for this relationship, nor they take into consideration quantitative correlation of such components. Scientific novelty and practical significance. Based on the relationship determined between the main components, a new oil production classification have been developed allowing to choose sustainable field development strategy and production methods as early as at the initial exploration stage, extend time between workovers in wells by mitigating production problems, minimizing geological risks related to bringing wells back on production after scheduled repair or workover operations, and increasing cost efficiency of oil and gas deposits development.

A case study for cost-effective design of relocatable deep dewpoint control gas plant

Australia has significant smaller-capacity gas fields, in relatively remote areas. An economically viable design for the Australian market is a small to mid-size gas plant to produce pipeline-quality gas and recover attractive amounts of liquid products (NGLs) for export by truck. Such a plant has minimal equipment, is highly modularised to be cost-effective for remote locations with high labour costs, can be relocated, and can be implemented in a substantially shorter time frame than conventional projects. For the North and South American markets, we have developed a deep dewpointing process that combines high NGL recovery with simplicity of design, yet is flexible enough to accommodate a range of compositions and flow rates. This design is well suited for standardisation of small to medium-size gas plants where feed gas compositions may vary and capacity increases are not well known. A short implementation schedule provides first-to-market economic benefits. We have developed 3rd Generation ModularisationSM that is proven to significantly reduce a plant’s footprint compared with more traditional modularisation practices. This new approach makes it possible to design a gas processing facility as transportable modules that can be built in the most cost-effective location, are low cost to install and may be relocated in the future. This has been demonstrated in a recent project completed in 2015 for Shell in Canada. This paper presents the solution for the Australian market that combines the benefits of high gas liquids recovery with low investment, delivered in compact relocatable modules that enable very flexible field development strategies.

Upscaled Pseudoporosity Estimated From Seismic-Acoustic-Impedance Volume Can Effectively Predict Gas Fingering in a Dynamic Model: A Gas-Injection Case Study

Summary Gas injection in an unsaturated carbonate reservoir that is in southwest Iran started in 2009, and, by use of the original dynamic model, the gas breakthrough would not have happened earlier than 2017. However, the gas/oil ratio (GOR) in some wells increased rapidly before the anticipated time, and the real-field data show different behaviors of gas breakthrough in different wells. The 3D geocellular grid obtained from the acoustic-impedance volume was applied to the dynamic model, and by making the size of the cells smaller, heterogeneity was identified as the most-important factor in modeling and monitoring the gas movement from the injected wells toward the production wells. On the basis of the new upscaling, two dynamic models were constructed. The original model had 267,000 grid cells, and the new ones have 1,120,000 and 2,400,000 grid cells, respectively. Running the new models illustrated the gas breakout from some wells mostly through one layer of the reservoir. Because effective porosity was estimated by collocated co-Kriging of well data with seismic porosity (upscaled pseudoporosity), vuggy-porosity distribution in this layer is correctly predicted, given that a karstic volume has been detected in some parts of the reservoir, the boundary of which has been defined by seismic data, and the crossplots of neutron porosity with acoustic impedence validate the reliability of this karstic zone. The results show various types of vuggy porosity that mostly govern the productivity of the wells in this reservoir. However, two main dissolution events in the reservoir can be recognized: early diagenetic dissolution that created secondary pores as intercrystaline/microporosity that are not significant in our reservoir; and a paleokarst event that generated secondary pores as molds and large vugs (up to 2 cm in size). As a result of this study, both dynamic and static models were modified by use of pseudoporosity derived from seismic data as the best-known distributed heterogeneity in this field to predict the gas movement in the reservoir. On the basis of this modeling, new maximum efficient rates were defined for wells and the field-development strategy was reconsidered.

Structuring an artificial intelligence based decision making tool for cyclic steam stimulation processes

Cyclic steam stimulation (CSS) is one of the more popular EOR techniques due to the existence of giant heavy oil reserves existing in different parts of the world. Numerical reservoir simulation plays a critical role in investigating the mechanisms and optimizing field development strategies of CSS processes. An artificial neural network (ANN) based model is considered to be a powerful subsidiary tool of high fidelity models for its fast computational speed and reliable prediction capability. This work focuses on the development of a robust surrogate model as a screening/design tool for cyclic steam injection processes using artificial neural network technology.The major contribution of this work includes training of the ANN model using a network topology optimization workflow to help the ANN better understand the complex data structures that are encountered in such processes. The developed ANN model successfully incorporates rock-fluid properties such as relative permeability and temperature dependent viscosity as input parameters together with the other relevant data. Last but not least, the network model utilizes a hybrid structure to adapt to the automatic cycle switching scheme that can be encountered in cyclic steam injection processes. The paper shows that the ANN model can be employed both as a classification tool and a nonlinear regression tool. The model is validated via extensive blind tests against high fidelity simulation models and can be used as a powerful screening and process design tool in global optimization of the process.

Characterization of Reservoir Heterogeneity by Capacitance-resistance Model in Water-flooding Projects

Tedious calculations and simulations are needed to obtain an efficient production scenario and/orproper field development strategy. Capacitance-resistance model (CRM) is proved to be a fastreservoir simulation tool using just the field-available data of production and injection rates. Thisapproach sets a time-constant and a weighting factor (or well-pair connectivity parameter) betweeneach pair of injection and production wells according to their histories. In this study, we investigatedthe behavior of the CRM parameters in synthetic reservoir models with different porosity andpermeability maps. Four reservoirs are considered with different porosities and permeabilities to studytheir effects on CRM response. We defined a new parameter, named error to mean production ratio(EMPR), to analyze the CRM performance. Some fluctuations are exerted on the production data toevaluate the capability of CRM against variable production records. Porosity showed a stronger effecton CRM parameters than the permeability based on the calculated EMPR. Unstable productionhistory would result in large error which can be corrected with some smoothing techniques onvariable production data. Also, a linear trend of EMPR was obtained with the change of porosity andpermeability or a combination of the two parameters within the reservoir.

New theoretical model for predicting and modelling fractures in folded fractured reservoirs

In this study, an attempt has been made to develop a new theoretical model that can be used to predict the fracture spacing/density that develops in a single competent layer and in multilayers as a result of folding. The work is based on earlier analyses concerned with the fracturing of unfolded strata subjected only to layer-normal compression. Such a stress state exists in the upper crust in any tectonically relaxed region where the principal cause of stress is the overburden. Unlike previous studies on theoretical fracture-spacing modelling that are mainly designed for a layer-parallel horizontal-extension system, this study has introduced a new theoretical model for the predicting and modelling of fracture spacing/density in ‘folded’ reservoirs, which contain > 85% of the world's oil and gas traps. This theoretical model is an integrated model: that is, it takes into account both rock mechanical and geometrical properties of the reservoir. The big advantage of the theoretical model developed in this study is that it provides well- and reservoir-scale estimates of the fracture spacing, for both axial and cross-axial fractures (i.e. the dominant fracture sets in folded reservoirs), which can be used for predicting fracture density (the reciprocal of fracture spacing), fracture aperture, the Rock Fracture Potential Index (RFPI), fracture porosity, fracture permeability, the shape factor (sigma) and for optimizing the drilling (i.e. the Optimum Drilling Direction (ODD) and the Optimum Drilling Angle (ODA) to maximize the fracture intersection in the wells) in three dimensions in folded, single layer and multilayer fractured reservoirs. In addition, new approaches are described for quantifying the mechanical bed thickness (MBT) or mechanical unit thickness (MUT), estimating the fracture aperture ( w ), estimating the distance from the neutral surface ( a ) and determining the RFPI data that are essential for implementing the theoretical model presented in this paper related to subsurface, folded, fractured reservoirs. The expressions derived for fracture spacing/density, for both axial and cross-axial fracture sets, involve data that are always available for every field development (i.e. seismic, well and core data). An understanding of the distribution of the fracture spacing/density, fracture aperture and the RFPI at an early stage in the development of a fractured reservoir is crucial in selecting a proper field development strategy, managing well placement and for monitoring production from the reservoir. In summary, based on several case studies, one of which is presented in this paper, it can be confirmed that the theoretical model, expressed in equations given in this paper, predicts what has been observed in the folded clastic reservoirs of the study area. It is concluded that curvature alone cannot reveal the location of natural fractures in a reservoir and that the mechanical properties of the reservoir rock play a significant role in the development of a natural fracture system. Rock can accommodate strain by fracturing (i.e. if its RFPI is high) or (if its RFPI is low) use its internal strain storage capability (associated with its mechanical properties: e.g. porosity collapsing, grain sliding and the formation of intra-grain hairline fractures) to consume the stress without the need to accommodate strain by fracturing.

Integrated Reservoir Characterization and 3D Modeling of the Monteith Formation: A Case Study of Tight Gas Sandstones in the Western Canada Sedimentary Basin, Alberta, Canada

SummaryThe Monteith Formation is an important tight gas reservoir in the Deep Basin, Alberta, and consists of a progradational succession of shallow marine sediments, nonmarine carbonaceous and coaly, coastal plain facies, and coarse-grained fluvial deposits, from base to top, respectively. This study is based on multiscale description and characterization techniques with cores and drill cuttings, including multimethods laboratory measurements of key reservoir parameters such as porosity and permeability. A second stage of the study involves the use of laboratory measurements obtained from cores and drill cuttings and their integration with well logs to construct a numerical 3D model of the study area. The 3D model is used to history match gas production, and forecast performance of new wells in those areas where the geologic model indicates potential for gas production.The ultimate goal is to provide a better understanding of the distribution of reservoir properties in the study area for developing drilling prospects and their production potential in areas where reliable data are scarce. The reservoir-modeling stage is carried out by implementing a recently developed methodology that integrates a variable shape distribution (VSD) model, capable of capturing different reservoir properties throughout the whole scale spectrum without any data truncation. Truncation is the excuse generally used for eliminating information that does not fit a given distribution. The claim is that the data are of poor quality, something that is not true in many cases. This new methodology eliminates the need for truncation, and introduces an extension of the VSD approach for reservoir-simulation purposes that reduces uncertainty in the generation of drilling prospects.Core analysis shows that the Monteith A member is composed of complex fluvial-dominated deposits with better rock quality than the shallow marine sandstones of the Monteith C member. This is most likely because of larger pore-throat apertures that range between 0.5 and 1 μm, and a relatively higher proportion of preserved intergranular pore space within these coarser-grained framework grains. Furthermore, the best production performance is from wells that are producing from the Monteith A. Variability of production rates also seems to be controlled by the presence of natural fractures. It is anticipated that the resulting 3D reservoir model will allow improving field-development strategies for this and other similar unconventional gas reservoirs in the Deep Basin of Alberta and elsewhere.