Gas-oil Ratio Research Articles

Study of the Influence of Well Operation Parameters of a Carbonate Reservoir Oil Formation on the Coefficient of Productivity Using Statistical Methods of Analysis

Well productivity index is one of the most important indicators for the development of carbonate reservoirs of oil fields, control and maintenance of high values of which determines the levels of hydrocarbon production. Determination of the complex influence of geological and technological factors on production capabilities of wells remains an actual direction of research in the field of oil producing. The present paper is devoted to improving the efficiency of production wells in a carbonate reservoir oil deposit based on the results of evaluation and consideration of the relationship between the productivity index and geological and field parameters such as reservoir pressure, bottomhole pressure, skin-factor, gas-oil ratio, water cut, using statistical methods of analysis. At the stage of preparation of initial data the materials of hydrodynamic and production-geophysical studies performed on the wells during the whole period of development of oil reservoir of one of the fields of Perm region were involved. The analysis of the obtained data sample with the use of statistical methods allowed us to study the relationships between the specific well productivity index and the considered geological and production parameters. Multivariate statistical models were developed using stepby-step regression analysis, collectively demonstrating the predominant influence of bottomhole pressure, reservoir pressure and water cut on the specific well productivity index based on the frequencies of occurrence of parameters and the order of their inclusion in the model. The study of the dynamics of changes in the accumulated multiple correlation coefficient during the development of statistical models allowed us to identify the ranges (areas) of change in the values of the specific well productivity index, which are characterized by individual correlations with geological and production parameters described by the corresponding mathematical dependencies. The developed models are characterized by high quality, which is confirmed by their statistical evaluations when comparing forecast and factual values of specific well productivity index. The criteria of applicability of models for conditions of carbonate reservoirs of oil fields are formed. The results of the study can be used for justification and regulation of technological modes of well operation, planning programs of optimization measures.

A New Transformation Method of the T2 Spectrum Based on Ordered Clustering—A Case Study on the Pore-Throat Utilization Rule of Supercritical CO2 Flooding in Low Permeability Cores

Nuclear magnetic resonance (NMR) and high-pressure mercury injection (HPMI) have been widely used as common characterization methods of pore-throat. It is generally believed that there is a power function relationship between transverse relaxation time (T2) and pore-throat radius (r), but the segmentation process of the pore-throat interval is subjective, which affects the conversion accuracy. In this paper, ordered clustering is used to improve the existing segmentation method of the pore-throat interval, eliminate the subjectivity in the segmentation process, and obtain a more accurate distribution curve of the pore-throat. For the three kinds of cores with ordinary-low permeability (K > 1 mD), ultra-low permeability (0.1 mD < K < 1 mD), and super-low permeability (K < 0.1 mD), the pore-throat distribution curves of the cores were obtained by using the improved T2 conversion method. Then, the oil and gas two-phase displacement experiment was carried out to investigate the degree of recovery and cumulative gas–oil ratio changes during the displacement process. Finally, the converted T2 spectrum was used to quantify the utilization of different pore sizes. The improved T2 conversion method not only has better accuracy but also is not limited by the pore-throat distribution types (such as unimodal, bimodal, and multi-modal, etc.) and is suitable for any core with measured HPMI pore-throat distribution and an NMR T2 spectrum. Combined with the results of core displacement and the degree of pore-throat utilization, it is found that the potential of miscible flooding to improve the recovery degree is in the order of ordinary-low permeability core (18–22%), ultra-low permeability core (25–29%), and super-low permeability core (8–12%). The utilization degree of immiscible flooding to the <10 nm pore-throat is low (up to 35%), while miscible flooding can effectively use the <3.7 nm pore-throat (up to 73%). The development effect of supercritical CO2 flooding on K < 0.1 mD reservoirs is not good, the seepage resistance of CO2 is large, the miscible flooding makes it difficult to improve the recovery degree, and the utilization effect of pore-throat is poor.

Predicting Bubble Point Pressure and Gas-Oil Ratio PVT Properties Using Bagging Ensemble Techniques

An enhanced accurate predictive model has been developed for the estimation of reservoir oil Pressure-Volume-Temperature (PVT) properties of Bubble Point Pressure (BPP) and Gas-Oil Ratio (GOR) using Bagging Ensemble machine learning. To develop the Bagging ensemble (BE) model, three different estimators, Decision Tree Regressor, Random Forest Regressor, and Extra Trees Regressor, were used as the base estimators. An averaging method to finally predict the model performance was done using a voting regressor to fit the base estimators. Hyper-parameter tunings for optimization were determined using cross-validation grid search and the implementation of Bagging ensemble described. The ensemble methods were compared with those developed using Artificial Neural Network (ANN) and some selected empirical correlations. Their performances were evaluated using Average Absolute Percentage Relative Error (AAPRE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and graphical cross-plot error analysis Correlation Coefficient (R2). Result showed improved performances, notably the R2 for BPP, the BE is 96.6%, ANN is 89.04%, and the best empirical model is 88.1%. For the GOR, the R2 for BE and ANN are 94.1% and 89.0%, respectively while best empirical model is 88.1%.

Heterogeneity Modeling and Heterogeneity-Based Upscaling for Reservoir Characterization and Simulation

Summary Reservoir heterogeneity is a key factor in modeling reservoir performance. Heterogeneity measures can be calculated for a given permeability field but are not sufficiently straightforward to reverse the process. Detailed heterogeneity can be built into a fine-scale model but can be lost during upscaling to a coarse scale, no matter which method is chosen from simple averaging to flow-based (FB). In this paper, we propose a method of heterogeneity modeling and heterogeneity-based upscaling with the aim of solving these problems. Unlike the traditional geostatistical method used to generate a permeability field that is not directly linked to a desired heterogeneity coefficient, the proposed method creates a heterogeneous permeability field directly using a Lorenz coefficient. Using a given Lorenz coefficient as an input, the expected heterogeneous permeability field can be generated via the proposed steps and equations. Using the proposed heterogeneity-based upscaling method, the Lorenz coefficient and curve, defined from the fine-scale model, can be preserved with minimum heterogeneity loss after upscaling such that gas/oil ratio (GOR) can be matched between the fine- and coarse-scale models without using pseudofunctions. The proposed method has been applied successfully in modeling a giant carbonate oil field in the Caspian Sea consisting of a matrix-dominated platform and a fracture/karst-dominated rim. Due to the field’s complex geology and high H2S content, a dual-porosity/dual-permeability compositional model has been created to model compositional flow within/between matrix and fracture/karst initialized with an abnormally high reservoir pressure. The field surveillance data show that reservoir heterogeneity is a key component for the field reservoir characterization and simulation. The Lorenz coefficient and curves can be estimated from the cores and logs, but the challenge is how to preserve the characteristics of the Lorenz coefficient and curves during modeling, upscaling, history matching, and uncertainty analysis (UA). Application of the new method has demonstrated its ability to overcome this challenge and has significantly improved the quality of the field’s reservoir modeling, upscaling, history matching, and UA. The fine-scale model Lorenz coefficient and curves were directly applied to calculate the coarse-scale permeability. The range of the Lorenz coefficient and curves estimated from cores and logs were used to generate a range of heterogeneous permeability fields for UA. Regional Lorenz coefficients were adjusted based on the mismatches of GOR, and new heterogeneous permeability fields were generated to improve the history-matching quality.

Segregating Laterals for Efficient Gas Re-Injection in Shale Plays Using Smart Completion

There is a strong global demand for oil and gas resources, and forecasts indicate robust growth in oil demand in the coming years. Meeting this demand necessitates the exploitation of unconventional resources and enhancing the recovery of existing oil and gas fields. Field trials indicate that traditional gas injection in shale wells has low sweeping efficiency. Emerging technologies play an exceedingly significant role in solving the challenges of gas EOR in shale and tight formations. Among these advancements, smart or intelligent well technology has emerged as a promising solution to enhance field development outcomes. This study focuses on improving gas flooding efficiency in the Bakken formation by utilizing smart completions to reduce the gas–oil ratio (GOR) and increase oil recovery. An economic assessment of gas re-injection is conducted, considering both gas storage and enhanced oil recovery, with analysis incorporating capital expenditures, operating costs, and revenue from increased production. Reservoir simulations were employed to determine the most effective gas injection scenarios for maximizing recovery and storage efficiency. Simulation results demonstrate that 20% of perforated laterals account for 80% of the injected gas. To address this challenge, this work proposes using smart completions to segregate lateral sections, thereby optimizing gas injection efficiency, and unlocking additional oil in tight formations. Segregating horizontal laterals for gas re-injection using smart completion technology can achieve gas injection efficiencies of up to 0.25 barrels per Mcf of gas injected, with lower incremental gas production. The optimal injection rate is between 1 MMcfd and 3 MMcfd, with an injection period ranging from one to three years. It was also found that injecting gas into the toe section results in high bottom hole pressure but lower oil recovery due to reduced gas injection efficiency. From an economic perspective, the project yielded favorable outcomes, with a positive net present value (NPV) at a 7% discount rate. Even at lower oil prices (USD 50 per barrel), the Internal Rate of Return (IRR) was calculated to be 170%, indicating strong potential profitability.

Physics-based, data-driven production forecasting in the Utica and Point Pleasant Formation

We introduce a robust, data-driven and physics-based method to forecast the estimated ultimate recovery (EUR) of gas and condensates from the Utica-Point Pleasant Formation. By categorizing 3,410 horizontal wells into eight static cohorts based on the initial gas-oil ratios (GOR) and completion dates, we construct generalized extreme value (GEV) distributions of annual reservoir fluid mass production for each cohort. We use the expected values (means) to build historical, statistical well prototypes for each cohort. These prototypes account for hydraulic fracture deterioration, pressure interference between neighboring fractures, well interference, and advancements in completion technology. We extrapolate mass production of the reservoir fluids for several decades by fitting the scaling parameters of our physics-based model to the statistical well prototypes. A key innovation in this approach is using GEV statistics to generate a time series for each cohort’s GOR and condensate-gas ratio (CGR). We replace the individual production rates from all existing wells with their corresponding extended well prototypes and convert mass to volumetric rates. We sum up the latter rates and provide a base forecast of total reservoir fluid rates and cumulative production, closely matching the historical field production. Our base forecasts predict approximately 23 trillion scf of gas and 200 million barrels of condensate by 2035.The Utica-Point Pleasant Formation is divided into core and noncore areas, each further subdivided into regions based on fluid types. We analyze the future infill potential per square mile in each of these regions, proposing two drilling schedules and four potential future drilling scenarios. We identify approximately 35,847 potential future wells: 12,476 in the core area and 23,371 in the noncore area. The core wells can contribute about 157 trillion scf of natural gas and 0.9 billion barrels of condensate.This study marks the first integrated evaluation of the future production from the Utica-Point Pleasant Formation by combining GEV statistics, physics-based modeling, big-data analysis, geology, proposed drilling programs, and economics. It provides a comprehensive understanding of the Utica-Point Pleasant Formation’s significance in future U.S. shale production.

Study on CO2-Enhanced Oil Recovery and Storage in Near-Depleted Edge–Bottom Water Reservoirs

The geological storage of carbon dioxide (CO2) is a crucial technology for mitigating global temperature rise. Near-depleted edge–bottom water reservoirs are attractive targets for CO2 storage, as they can not only enhance oil recovery (EOR) but also provide important potential candidates for geological storage. This study investigated CO2-enhanced oil recovery and storage for a typical near-depleted edge–bottom water reservoir that had been developed for a long time with a recovery factor of 51.93%. To improve the oil recovery and CO2 storage, new production scenarios were explored. At the near-depleted stage, by comparing the four different scenarios of water injection, gas injection, water-alternating-gas injection, and bi-directional injection, the highest additional recovery of 3.62% was achieved via the bi-directional injection scenario. Increasing the injection pressure led to a higher gas–oil ratio and liquid production rate. After shifting from the near-depleted to the depleted stage, the most effective approach to improving CO2 storage capacity was to increase reservoir pressure. At 1.4 times the initial reservoir pressure, the maximum storage capacity was 6.52 × 108 m3. However, excessive pressure boosting posed potential storage and leakage risks. Therefore, lower injection rates and longer intermittent injections were expected to achieve a larger amount of long-term CO2 storage. Through the numerical simulation study, a gas injection rate of 80,000 m3/day and a schedule of 4–6 years injection with 1 year shut-in were shown to be effective for the case considered. During 31 years of CO2 injection, the percentage of dissolved CO2 increased from 5.46% to 6.23% during the near-depleted period, and to 7.76% during the depleted period. This study acts as a guide for the CO2 geological storage of typical near-depleted edge–bottom water reservoirs.

Combined use of petroleum inclusion analysis, PVT simulation, and basin modeling for reconstruction of deep fluid phase evolution in condensate gas reservoirs

The reconstruction of the fluid phase evolution in deep condensate gas reservoirs can reveal the mechanism of condensate gas formation, facilitating the early formulation of drilling strategies. However, the complexity of petroleum fluid phase evolution during hydrocarbon generation, migration, and accumulation poses numerous challenges for the reconstruction process. Therefore, petroleum fluid inclusion analysis, PVT phase simulation, and basin modeling were used to achieve the reconstruction of phase states during key geological periods, elucidating the phase evolution of the deep condensate reservoirs in the Dongying Depression during the whole process. The modeled results show that the mature source rocks contributed to the charging and accumulation of liquid oils (38–14 Ma). Next, a low oil cracking conversion rate limited the increase of gaseous hydrocarbon fraction, so the accumulated hydrocarbons remained in a liquid phase (14–0 Ma). The late external gas inputs significantly increased the gas-oil ratio in the reservoirs, leading to the transition from the liquid oil phase to the condensate phase (5–0 Ma). The fluid compositions obtained from hydrocarbon inclusions and the physical properties of present-day condensates can effectively constrain basin modeling, leading to reliable simulation results. This work revealed that the hydrocarbon generation controls the initial hydrocarbon components in the traps for the phase evolution. Furthermore, the secondary alterations including oil cracking and gas inputs influence the proportion of methane of petroleum in the deep reservoirs, which dominates the phase evolution. Deep petroleum fluid phase changes mainly require the molar ratio of the input gas more than 50%. A model was proposed to explain the formation of deep condensate reservoirs. A series of gas inputs and escape in the successive lithological traps controls an orderly phase change of deep petroleum, and the amount of deeper gas determines the range of the existence of condensate gas reservoirs. This study not only guides the exploration of deep condensate in the Dongying Depression but also offers a workflow for the research on the formation and evolution of condensate reservoirs in other global regions.

Application of FLAIR technology in evaluation of complex fluids—Taking M structure of Huizhou sag as an example

Abstract Due to the complexity of reservoir fluid types and the difficulty in identifying hydrocarbon abundance in M structure of the Huizhou sag, the conventional gas logging methods are affected by instrument accuracy, resulting in indistrument difference in gas logging response characteristics. Consequently, the traditional evaluation methoda are inadequate.To address these challenges, fluid logging & analysis in real time technology(FLAIR) is applied. This technology quantifies various gas components by detecting the ion mass, offering advantages such as precise detection and wide measurement range.In this paper,the data of FLAIR technology was analyzed. Based on the differences in hydrocarbon components of various fluids, fluid identification charts were developed using the hydrocarbon component ratio, humidity ratio (WH) and equilibrium ratio (BH) parameters, which could effectively distinguish complex fluids such as gas, condensate, oil and volatile oil formations.The oil index and gas index were selected, and the gas-oil ratio index was formulated. A relationship model between this parameter and the gas-oil ratio data, measured through cable sampling and formation testing was established, enabling the prediction of gas-oil ratio data during drilling. By correcting gas data to reduce the influence of factors such as drilling time, displacement volume and bit size through gas data correction, the evaluation chart of oil and gas abundance, based on the anomaly ratio of gas measurement can adress the evaluation challenges of buried hill reservoir. The aforementioned method provides robust technical support for reservoir fluid identification and evaluation in M structure of Huizhou sag, and has significant application value in complex reservoirs and complex fluid conditions.

Data-Driven Method for Predicting the Transportable Maximum Gas–Oil Ratio

Data-Driven Method for Predicting the Transportable Maximum Gas–Oil Ratio

Study Evaluates Long-Term Corrosion Risk of Load-Bearing, High-Power ESP Cable

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 34898, “Long-Term Corrosion-Risk Evaluation of Load-Bearing, High-Power Electrical Submersible Pump Cable,” by Serko A. Sarian, SPE, Alkhorayef Petroleum, and Hattan Banjar, SPE, and Chidirim Ejim, SPE, Saudi Aramco, et al. The paper has not been peer reviewed. Copyright 2024 Offshore Technology Conference. _ In this study, a fit-for-purpose electrical submersible pump (ESP) load-bearing power cable was deployed inside the production tubing of a sour oil well for an extended period of service. Project economics required the power-cable metallurgy to survive long-term exposure reliably for up to 15% hydrogen sulfide (H2S) and high-salinity production fluids. Laboratory tests simulating varying sour well and extended galvanic corrosion conditions have been performed to determine the adequacy of the selected armor metallurgy in downhole corrosive environments. Introduction In recent years, various types of load-bearing power cable-deployed ESP (CDESP) systems have been introduced, eliminating the need to install or remove a conventional ESP along with the completion and reducing ESP changeover time by more than 80%. More recently, live-well CDESP systems have been developed and tested to eliminate the need for a well-kill operation. The most-critical and costly component of such a system is the load-bearing power cable. The cable has the dual purpose of conveyance and long-term powering-up of the ESP. In a conventional artificial lift operation, the ESP is conveyed using the production tubing with a non-load-bearing power cable strapped to the outside of the tubing. This operation requires a workover rig to remove and reinstall the entire completion and a killed-well operation that might permanently damage the reservoir. The main advantages of a CDESP system include operational efficiency gain and cost reduction, no reservoir skin damage, and production-loss minimization. However, a few challenges exist with the use of CDESP. These cables must have significantly different structures from commercial oil and gas power cables. These differences are detailed in the complete paper. Cable-Design Requirements CDESP system criteria include multiple-year continued service in adverse oilwell and reservoir conditions such as sour fluids, high gas/oil ratios (GOR) and temperatures, and abrasion caused by solids production. The following product acceptance requirements were set in advance: - Three redeployments or retrievals after the initial deployment - Single deployment continued runtime to meet or exceed the regional conventional ESP system runtime Based on regional statistics, a conventional ESP system single-deployment average run time is 2.5 years. The effective CDESP power-cable service life target then becomes 2.5 years × four total deployments = 10 years, which served as the basis for all cable-associated testing and reliability metrics.

Data-Analytics-Driven Sectorization Review Enables Efficient Reservoir Management

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 216359, “Data-Analytics-Driven Subsurface Reservoir Sectorization Review of a Giant Abu Dhabi Onshore Field Carbonate Reservoir for Efficient Reservoir Management,” by Khalid Javid, Sara Alshkeili, and Fatema Al-Hammadi, ADNOC, et al. The paper has not been peer reviewed. _ The complete paper discusses an approach used to sectorize a mature onshore Abu Dhabi giant carbonate reservoir for the purposes of reservoir management, offtake, and injection balancing. The reservoir is developed under a line-drive water-alternating-gas (WAG) scheme in combination with peripheral water injection. The development plan for the reservoir includes massive deployment of maximum reservoir contact (MRC) horizontal wells and a gas-based enhanced oil recovery scheme. A practical data-driven approach, based on one of the operator’s integrated reservoir management (IRM) workflows, was selected for the purpose of sectorization. Field Development Background Reservoir A is part of a giant oil field considered low permeability. Its porosity ranges between 13 and 26%, while permeability increases from 1 md on the flanks to 50 md in the crest of the structure. Production began 30 years ago, and the development scheme has undergone several transformations since. Currently, it is being developed under mid-flank line-drive waterflooding combined with crestal WAG and peripheral water injection. The current recovery factor of Reservoir A is approximately 10%. Two field-trial pilots were implemented (gas-injection and water-injection pilots) and then were converted to WAG injection between 2007 and 2013. These locations show high gas/oil ratio (GOR) and high water cut (WC). The development scheme includes downspacing of the existing line-drive to approximately 300 m intended to enhance sweep efficiency and increase recovery factor. The number of wells is expected to increase substantially. To optimize well placement and reduce development capital expenditure and subsurface congestion with other development targets in the field, development will capitalize on long horizontal MRC wells. Reservoir Sectorization Objectives. The sectorization exercise undertaken for Reservoir A aimed to achieve the following: - Establish a manageable number of sectors and allow creation of relevant, meaningful reservoir management key performance indicators and more-efficient surveillance programs - Honor main geological features -Reflect reservoir pressure distribution and communication between different areas of the reservoir - Reflect fluid-front movement - Reflect well-performance trends - Honor future development plans (including MRC wells) Proposed Sectorization Options. Reservoir A originally was divided into 13 regions for reservoir management, surveillance, and reservoir monitoring planning purposes, including production and injection balancing, voidage replacement ratio (VRR), pressure performance, and diagnostic plots. At the start of the study, eight sectorization scenarios were proposed. These scenarios were evaluated following a methodology described in the complete paper, aiming to combine the initial 13 regions into a smaller number of sectors where possible. Each scenario was evaluated against criteria including data analytics; the scheme honoring most of the criteria with an optimal number of sectors would be selected.

Main Challenges and Strategies to Develop Santos Basin Sub-Salt Fields, Deep water Brazil

Abstract Santos basin of Brazil is a hot area of oil and gas exploration and development in the world. Many sub-salt oil fields have been discovered in recent years. The reservoirs are mainly composed of microbial carbonate rocks with strong heterogeneity, which has developed a variety of storage space types. The fluid is complex with high gas oil ratio and CO2 content. The re-injection of produced gas brings uncertainty to the production performance and the stability of facilities. The project investment is huge due to many factors such as deep-water, deep buried and far away from the coastline. The complexity of reservoir and high investment of project make the economic and effective development of reservoir full of great challenges. This paper describes the development strategies of Brazil’s sub-salt deep-water oilfield and the main successful methods to maximize recovery and profits under uncertain conditions. It includes the following three aspects: (1) abundant information acquisition to reduce the main uncertainties; (2) development plan with robustness and flexibility to deal with potential risks; (3) multidisciplinary comprehensive research to evaluate reservoir mode and development mode.

Exploration and Practice of Synergistic and Efficient Development Technology for Gas-Capped Condensate Gas Reservoirs and Oil Rings

Abstract The oil-ringed condensate gas reservoir is one of the more unique and complex types of oil and gas reservoirs. The stable oil and gas interface is crucial for the efficient development of such reservoirs, and the fundamental means of addressing the issue lies in maintaining the dynamic balance between the gas cap and the oil ring. In this paper, we address issues such as the rapid decline in oil ring production, gas cap breakthrough, and increased condensate oil loss exposed during the development of the R carbonate reservoir in the Caspian Sea Basin of Kazakhstan. Using reservoir engineering and numerical simulation methods, we optimized synergistic development ideas and the boundaries of exploitation policies for gas-capped condensate gas reservoirs and oil rings. We formulated reasonable development parameters such as extraction rate, injection-production ratio, barrier water injection ratio, and proposed dynamic control standards for zoning and classification. After implementing the plan, the gas-oil ratio of the oil ring significantly decreased, with a reduction rate of 12 percentage points. The gas production per unit pressure drop increased by 40%. The annual decline rate of condensate oil decreased from 23.6% to 13.8%. All development indicators exhibited characteristics of benign development, providing a theoretical foundation and technical methods for guiding the efficient development of similar oil and gas reservoirs.

Numerical Simulation Study on Flue Gas-Assisted Steam Huff and Puff in Heavy Oil Reservoirs.

In response to challenges such as the rapid rise of water content, the rapid decline of periodic production, and the serious intrusion of edge and bottom water in heavy oil reservoirs after multicycle profile control, the flue gas-assisted steam huff and puff technology is proposed. By focusing on Block Cao128 in the L Oilfield as a research subject, the feasibility of applying the flue gas-assisted steam huff and puff technology in heavy oil reservoirs has been verified through the establishment of a three-dimensional geological model. Additionally, the injection and production parameters in steam huff and puff have been optimized. The research results indicate that when the cyclic steam injection volume is 1000 t, the maximum net oil increment can be achieved, reaching 6308 × 104 t. Furthermore, when the cyclic flue gas injection volume is 9 × 104 Nm3, the efficacy of the flue gas-assisted steam huff and puff is enhanced. The optimal injection method is to inject flue gas in the early stage of steam huff and puff, and the oil-gas ratio is increased to 1.77. The introduction of flue gas can enhance production significantly and exert a significant impact on early-stage production. The earlier the injection time of flue gas, the better the development effect. Flue gas-assisted steam huff and puff technology improves energy utilization efficiency and reduces pollutant emission. It has a significant positive impact on environmental protection and sustainable development.

Characterizing hydrocarbon discoveries and prospects in the Tay Sandstone using relative elastic inversion: Greater Pilot area, Central North Sea

The Pilot oil accumulation and associated discoveries of Blocks 21/27 and 21/28 have been stranded assets for three decades. The principal cause of delay in developing the oil fields is because these are heavy oils with low gas–oil ratio and, in some cases, significant gas caps. The oil in the Tay Sandstone is trapped in structural and stratigraphic closures at a depth of 800–1300 m. Determining pay thickness and delimiting oil-in-place in some existing discoveries and especially in undrilled prospects has been problematic. This difficulty has been addressed by conducting an innovative amplitude versus offset (AVO) inversion of the seismic data which has been successfully blind tested against the wells and prognosed closures. The method, which is a band-limited form of extended elastic impedance (EEI), has proved robust for discriminating gas-, oil- and brine-filled sands from one another. Oil and gas trapped further down-dip in the discoveries Fyne, Crinan and Dandy are developed in Tay reservoirs showing generally lower net-to-gross. The sensitivity of the rEEI attribute to the presence of thicker intervals of oil pay, in this case, is used to indicate where higher net-to-gross is present above the oil–water contact, a characteristic of direct relevance to the planning of optimally located development wells.

Enhancing PVT property predictions for black oil reservoirs through the application of supervised machine learning techniques

The study presents an innovative approach utilizing machine learning (ML) techniques to forecast essential characteristics of black oil reservoirs, namely the oil formation volume factor (Bo) and the solution gas-oil ratio (Rs). A suite of four established ML algorithms-Random Forest, Linear Regression, Ada Boost, and Gradient Boost were extensively conditioned using a dataset that includes 297 individual data points and evaluation is done using test data and a blind test of models. The study showcases the practical efficacy of ML in accurately predicting Bo and Rs, even with a relatively modest dataset. The potential of ML models to save time and costs by reducing the dependence on laborious laboratory measurements is underscored, presenting a transformative shift in reservoir engineering practices. Furthermore, the current research extends its impact by comparing ML predictions with various empirical correlations and actual values, demonstrating that ML models, particularly Gradient Boost, consistently outperform traditional correlations and it can be used to predict values in the undersaturated region too. It is observed from the blind test results that for the prediction of Bo Gradient boost and random forest models (R2 0.97, RMSE 0.01) is a preferable choice and for the prediction of Rs the Gradient boost model (R2 0.97, RMSE 22) can be preferred. The adaptability of ML models to integrate new data over time is a significant feature, ensuring continuous improvement in accuracy. This study adds to the expanding reservoir engineering knowledge base, highlighting the practical use of machine learning for accurately predicting crucial reservoir properties. Its significance goes beyond theoretical progress, providing tangible tools for reservoir engineers and geologists to improve decision-making in the management of oil and gas reservoirs.

Solution Enables Well Surveillance in Stacked Reservoirs With Near-Critical Fluids

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 23912, “Digital Solution for Well Surveillance in Stacked Reservoirs With Near-Critical Fluid Systems,” by Kristian Mogensen, SPE, Carlos Mata, SPE, and Swarjit Samajpati, ADNOC, et al. The paper has not been peer reviewed. Copyright 2024 International Petroleum Technology Conference. _ Fully compositional integrated asset models (FC-IAMs) are being deployed for an increasing number of fields in the operator’s portfolio. The complete paper describes the development of comprehensive digital well surveillance and field-production optimization for an offshore field consisting of four stacked reservoirs, each containing near-critical fluids. Augmenting the FC-IAM with high-frequency sensor data in addition to proprietary tools to actively monitor well performance helps identify and pursue opportunities to maximize oil production from the field, subject to several system constraints. Introduction The field is approximately 120 km offshore Abu Dhabi. During the appraisal phase, eight wells penetrated the structure and confirmed that the predominant lithologies are composed of dolomite and anhydrite, with subordinate limestone. Production of oil and gas comes from four main reservoirs—A, B, C, and D. The ongoing development centers around miscible gas injection along the crestal part of each reservoir and peripheral water injection. Integrated asset models are one of many key initiatives supporting the operator’s Smart Production Growth initiative. Although the calculations identifying scope for improved production rates are firmly founded in science, substantial digitization efforts are required to automate, streamline, and sustain any such digital solution. Fig. 1 illustrates the fact that company database management plays a fundamental role in providing reliable input to keep models updated and fit for purpose. Fluid-Property Modeling Each of the four producing reservoirs contains near-critical fluids exhibiting a steep compositional depth gradient, which means that the solution gas/oil ratio (GOR) is well-specific and may change over time. The GOR trend is governed by several factors. The lightest part of the fluid column may be produced first because of the improved mobility; this would lead to a decreasing solution GOR over time. However, because of the crestal miscible gas injection, significant swelling will result in a gradual GOR increase followed by gas breakthrough. Solution GOR increases because of gas swelling. It is important to make the distinction between solution gas and free gas because the operational guidelines dictate that the reservoir pressure should be maintained above the saturation pressure and above the minimum miscibility pressure. Well-Test Validation Workflow The compositional framework adds an extra layer of complexity because fluid composition needs to be specified at the well level. A robust procedure is needed to first assign an initial reservoir fluid composition to each well. The second key task is to capture changes over time. The method of solution is referred to as a target GOR calculation. For any given fluid system, an equation of state (EOS) model must be tuned to experimental data to convert compositional information to black-oil properties at specific pressures and temperatures. A so-called anchoring fluid composition initially is assigned to the well, and the EOS can then calculate the resulting GOR and other variables from a simulated separator test. The evolved gas and liquid phases are then recombined mathematically to match a measured GOR from a production test. Matching is an iterative process but normally converges very quickly.

Study on Nonlinear Parameter Inversion and Numerical Simulation in Condensate Reservoirs

The B6 metamorphic buried hill condensate gas reservoir exhibits a highly compact matrix, leading to a rapid decline in bottom-hole pressure during initial production. The minimal difference between formation and saturation pressures results in severe retrograde condensation, with multiphase flow further increasing resistance. Conventional numerical simulations often overestimate reservoir energy supply due to their failure to account for this additional resistance, leading to inaccuracies in bottom-hole pressure predictions and gas–oil ratio during history matching. To address these challenges, this study conducted research on nonlinear numerical simulation for buried hill condensate gas reservoirs and established a method for calculating a multiphase pressure sweep range based on the well testing theory. By correcting and fitting the pressure propagation boundaries with numerical simulation, the nonlinear flow parameters applicable to the B6 gas field were inversed. This study revealed that conventional Darcy flow is inadequate for predicting pressure propagation boundaries and that it is possible to reasonably characterize the pressure sweep range through nonlinear flow. This approach resulted in an improvement in the accuracy of historical matching for bottom-hole pressure and gas–oil ratio, which improve the historical fitting accuracy to 85%, providing valuable insights for the development of similar reservoirs.

Study on fluid phase behavior characteristics of low permeability reservoir under different CO2 injection amounts

M reservoir is a low peremeability reservoir located in northwest China. Currently, it faces the problem of difficult water injection, low single well production, and rapid production decline. CO2 flooding technology is considered to be implemented to solve these problems by effectively supplementing formation energy. Considering that the phase behavior of reservoir fluid during CO2 injection and its influence on the development effect are complex, the PVT test experiment of reservoir fluid is carried out to analyze the variation of reservoir fluid properties under different CO2 injection volumes. The experimental results show that the saturation pressure, solution gas-oil ratio, volume coefficient and expansion coefficient of crude oil increase with the increase of CO2 injection, while the viscosity and density of oil continue to decrease. When the CO2 injection amount reaches 32 %, the dissolved gas-oil ratio increases from the original 36.2 m³/m³to 150.2 m³/m³, the viscosity of the formation oil is reduced by about 76 % than before CO2 injection; and the volume of crude oil has expanded to 1.28 times of that without CO2 injection. Moreover, the P-T phase diagram is drawn and the influence of CO2 injection on the distribution of phase regions in the phase diagram and the critical parameters of oil is analyzed. The high pressure physical properties of reservoir fluids at different CO2 injection volumes are obtained, which can provide a good foundation for the study of enhancing oil recovery by CO2 injection in M reservoir.