Production Logging Data Research Articles

Unraveling the hidden relationships between seismic multiattributes, well-dynamic data, and Brazilian pre-salt carbonate reservoirs productivity: A shallow versus deep machine-learning approach

During the initial phases of an E&P project, the most reliable data on the reservoir’s deliverability are acquired via drill stem tests (DST), which provide the productivity per flow unit whenever production logging tool data are available. However, DSTs are restricted to a few kilometers, whereas seismic data cover large areas. The integration of these data has been challenging, particularly due to the difference in scale between them. So, a new workflow to determine the relationship between poststack seismic attributes and reservoir productivity using classic supervised (shallow) and deep-learning regression algorithms was developed. The DST parameters were predicted over the entire seismic cube, which can be extremely valuable for the decision-making process. The data set is from the Brazilian deep-water presalt carbonate reservoirs of the Mero Field, which is a well-explored area with a plethora of test and production data. It is adjacent to an underexplored Central Libra appraisal plan area, which is covered by the same seismic survey. Thus, any relationships between seismic attributes and well productivity data observed at the Mero Field are extrapolated to the adjacent underexplored area. Ten seismic attributes and DST data from 10 wells of the Mero Field were used to train shallow and deep-learning supervised regression algorithms for the prediction of flow capacity and productivity index seismic cubes. Twenty development wells (blind tests) were used for the assessment of our predictive models. The highest percentage of correct predictions at the blind test wells (85%) was obtained with random forest regression using six attributes derived from a spectrally balanced full-stack volume, and neither amplitude-variation-with-offset nor inversion data were needed. Deep learning provided lower performance (75%) at a higher computational cost. It demonstrated a new reservoir derisking tool that can be used for project optimization in areas covered by the same seismic survey.

A machine learning-based simulation metamodeling method for dynamic scheduling in smart manufacturing systems

Conventional Digital Twins (DTs) in smart manufacturing rely on complex and time-intensive simulation models, hindering real-time DT-based decision-making. However, the availability of big data in Manufacturing Execution Systems (MES) enables training different Machine Learning (ML) models for fast and accurate predictions and decision assessments. Accordingly, this paper proposes an ML-Based Simulation Metamodeling Method (MLBSM) to facilitate DT-based decision-making for dynamic production scheduling in complex Stochastic Flexible Job Shop (SFJS) environments. The proposed MLBSM integrates three modules: a novel data vectorizing method (SPBM), multi-output Adaptive Boosting Regressor (ABR) models, and a new statistical risk evaluation method. SPBM converts unstructured production log data into numerical vectors for ABR training by calculating numeric penalty scores for each job based on the position of operations in the schedule queue. Each trained ABR predicts mean job completion times for various dynamic scenarios based on shift schedules. The risk evaluation method estimates the standard deviation of job completion times and calculates the delay probability scores for each job, aiding DT in promptly evaluating production schedules. Working seamlessly together, MLBSM modules present a novel way of using production log data for ML training and ultimately bypassing several computationally intensive simulation replications. In this research, a simulation model generates the synthetic MES data, focusing on the machining process at a photolithography workstation in the semiconductor manufacturing. Experiments demonstrate the MLBSM’s accuracy and efficiency, predicting high-risk jobs with over 80% recall and being at least 70 times faster than conventional simulation runs. Sensitivity analyses also confirm the MLBSM’s consistency under different workstation conditions.

Integrated Insights into Lithofacies, Fracture Networks, and Reservoir Productivity in Basement Reservoirs: A Comprehensive Case Study from the Cauvery Basin, India, Using Well Logs, Petrographic Studies, and Production Logs

Summary The architecture and heterogeneity of basement rocks can play a crucial role in reservoir evaluation and hydrocarbon exploration. This study focuses on the comprehensive characterization of basement reservoir heterogeneities and fracture networks in the Cauvery Basin using a combination of petrophysical and geochemical analyses. The intricate interplay of lithological variations and chemical alteration processes in basement rocks significantly influences their complex nature. In this study, openhole log data acquired in the basement section from wells within the Cauvery Basin were analyzed. These log data were correlated with core studies to understand the lithofacies heterogeneities present within the basement reservoir. Furthermore, fracture data obtained from resistivity image log data were examined to understand the distribution and properties of fractures in relation to lithofacies heterogeneities. Notably, a higher fracture density was observed in the mafic-rich sections compared to the felsic-rich sections. The integration of resistivity image log data with lithofacies contributed to the understanding of the reservoir architecture. Additionally, elemental spectroscopy logging facilitated geochemical analysis, providing measurements of the elemental composition of basement, which were incorporated into the study. Production logging (PL) was performed in the study wells to ascertain the production from the basement reservoir. This analytical approach augmented the characterization of the basement reservoir and contributed to a holistic understanding of its properties. This study emphasizes the role of conducting a thorough analysis of petrophysical log responses in conjunction with detailed core studies to characterize basement reservoirs. The study successfully differentiated two distinct lithofacies units within the Cauvery Basin’s basement reservoirs: mafic-mineral-dominated and felsic-mineral-dominated and their impact on reservoir heterogeneities. Production log data validated hydrocarbon contributions primarily from felsic rocks, underscoring the importance of lithological variations.

Integration of electromagnetic, resistivity-based and production logging data for validating lithofacies and permeability predictive models with tree ensemble algorithms in heterogeneous carbonate reservoirs

This study develops an innovative workflow to identify discrete lithofacies distributions with respect to the well-log records exploiting two tree-based ensemble learning algorithms: extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost). In the next step, the predicted discrete lithofacies distribution is further assessed with well-log data using an XGBoost regression to predict reservoir permeability. The input well-logging records are gamma ray, neutron porosity, bulk density, compressional slowness, and deep and shallow resistivity. These data originate from a carbonate reservoir in the Mishrif Basin of southern Iraq's oilfield. To achieve a solid prediction of lithofacies permeability, random subsampling cross-validation was applied to the original dataset to formulate two subsets: training for model tuning and testing for the prediction of subsets that are not observed during the model training. The values for the total correct percentage (TCP) of lithofacies predictions for the entire dataset and testing subset were 98 and 93% using the XGBoost algorithm, and 97 and 89% using the AdaBoost classifier, respectively. The XGBoost predictive models led in attaining the least uncertain lithofacies and permeability records for the cored data. For further validation, the predicted lithofacies and reservoir permeability were then compared with porosity–permeability values derived from the nuclear magnetic resonance (NMR) log, the secondary porosity of the full-bore micro imager (FMI) and the production contribution from the production–logging tool (PLT). Therefore, it is believed that the XGBoost model is capable of making accurate predictions of lithofacies and permeability for the same well's non-cored intervals and other non-cored wells in the investigated reservoir.

New insights into hypogenic non-matrix dissolution mega features driving subsurface fluid movement within the Arab Formation, Saudi Arabia

This paper reports the results of a large integrated study into the classification, distribution and significance of hypogenic non-matrix large-scale dissolution features present in the Arab Formation of Saudi Arabia. The investigation of these features was based primarily on extensive core, wireline caliper, wireline/log while drilling resistivity borehole image logs supported by encountered total lost circulation of drilling fluids and production log data. Dissolution features observed by this study were classified into three main categories: touching vugs, ramiform voids, and cavities. This study is the first to successfully quantify the presence and extent of these non-matrix intervals in this formation, which has greatly improved the 3D modelling efforts. Over 8100 feet of large-scale dissolution features were intersected in both vertical and horizontal wells. The hypogenic genesis of the large-scale dissolution features pre-dates replacement dolomitization in the area. Given the extensive data reviewed, the presented results give insight into the importance of the large-scale dissolution mega features as they are strongly related to total loss of mud circulation during drilling, and will have implications for well-to-well short-cutting during production and impact oil storage capacity.

Identifying Average Reservoir Pressure in Multilayered Oil Wells Using Selective Inflow Performance (SIP) Method

The downhole flow profiles of the wells with single production tubes and mixed flow from more than one layer can be complicated, making it challenging to obtain the average pressure of each layer independently. Production log data can be used to monitor the impacts of pressure depletion over time and to determine average pressure with the use of Selective Inflow Performance (SIP). The SIP technique provides a method of determining the steady state of inflow relationship for each individual layer. The well flows at different stabilized surface rates, and for each rate, a production log is run throughout the producing interval to record both downhole flow rates and flowing pressure. PVT data can be used to convert measured in-situ rates to surface conditions. Different types of Inflow Performance Relationship (IPR) equations can be used for SIP interpretation, including the Straight-line method, Fetkovitch method, and Laminar Internal Turbulent (LIT) relations. Although the SIP method can be used for single-phase flow, the interpreter can restrict the IPR’s calculations to a particular phase. This research discusses the difficulties in estimating the average reservoir pressure in multilayered reservoir completed wells over their production life. The SIP technique has been applied to some producing wells in the south of Iraq, which are completed in multiple producing reservoirs previously tested with a formation tester to estimate reservoir pressure and other parameters. Two wells are taken in the south of Iraq region, Zubair Oil Field, one with cross flow between perforations and the other well with no cross flow. An average pressure is not calculated for layer A in Well-1, because there is no contribution rate. While the average pressure for Well-1, layer B is 3414.49 psia. Also, the average pressure for Well-2, layer H is not calculated because there is no rate contribution from this layer, and the maximum average pressure was calculated in layer G, which is about 2606.26 psia. It is also found that the presence of cross flow has no effect on SIP calculations.

A copula-based statistical method of estimating production profile from temperature measurement for perforated horizontal wells

Estimating production profile from temperature data is of great significance for production increase and cost reduction in well production process. This study aims to provide a cost-effective direct method of interpreting flow profiles from temperature measurements. The concept of representative inspired by randomized distribution regarding heat conduction representative points for each no-influx section is proposed. Three assumptions regarding existence, distribution and independence of representatives are introduced to establish a copula-based statistical method of converting fitted distributions of representative, to distributions of relaxation distances for each no-influx section, and then to estimate the production profile. The method is applied on a horizontal gas well on the east slope of western Sichuan depression, and the simulated results are compared with the production logging data. The error of estimators for each no-influx section is between 6.7% and 11.8%, which is satisfactory. Sensitivity analyses are conducted regarding temperature measurement error, and the results verify the robustness of this method. This paper provides a reliable solution to interpret production profile from relatively inaccurate temperature data when the perforation zones are adjacently close. The findings of this study can help for better understanding of low-cost production allocation and uncertain flow state modeling problems.

Identification of Gas-Water Two-Phase Flow Patterns in Horizontal Wells of Shale Gas Reservoirs Based on Production Logging Data

In order to clarify the gas-water two-phase flow law in horizontal wells and study the gas-water two-phase flow characteristics in horizontal wells, firstly, the gas-water two-phase in a horizontal well is numerically simulated and analyzed, and the flow pattern distribution under different well inclination angles and different phase separation flow rates is obtained. Secondly, a series of production logging instruments including CAT instrument was used to conduct experimental research on gas-water two-phase flow under different flow conditions, and the measured values of each CAT probe were extracted to reflect the local holdup under different flow patterns. Finally, SSA-BP neural network algorithm is used to identify a gas-water two-phase flow pattern in a wellbore by using experimental parameters such as center holdup, well inclination angle, spinner revolution, and CAT probe measurements. The recognition accuracy of the neural network was improved from 83.75% to 91.66%, and the operation speed was accelerated. It provides a research idea to explore the flow characteristics of gas-water two-phase flow in horizontal wells.

Discrete fracture modeling by integrating image logs, seismic attributes, and production data: a case study from Ilam and Sarvak Formations, Danan Oilfield, southwest of Iran

Understanding the fracture patterns of hydrocarbon reservoirs is vital in the Zagros area of southwest of Iran as they are strongly affected by the collision of the Arabian and Iranian plates. It is essential to evaluate both primary and secondary (fracture) porosity and permeability to understand the fluid dynamics of the reservoirs. In this study, we adopted an integrated workflow to assess the influence of various fracture sets on the heterogeneous carbonate reservoir rocks of the Cenomanian–Santonian Bangestan group, including Ilam and upper Sarvak Formations. For this purpose, a combination of field data was used including seismic data, core data, open-hole well-logs, petrophysical interpretations, and reservoir dynamic data. FMI interpretation revealed that a substantial amount of secondary porosity exists in the Ilam and Sarvak Formations. The upper interval of Sarvak 1-2 (3491 m to 3510 m), Sarvak 1-3 (3530 m to 3550 m), and the base of Sarvak 2-1 are the most fractured intervals in the formation. The dominant stress regime in the study area is a combination of compressional and strike-slip system featuring reverse faults with a NW–SE orientation. From the depositional setting point of view, mid-ramp and inner-ramp show a higher concentration of fractures compared to open marine environment. Fracture permeability was modeled iteratively to establish a realistic match with production log data. The results indicate that secondary permeability has a significant influence on the productivity of wells in the study area.

Drilling Cores and Geophysical Characteristics of Gas Hydrate-Bearing Sediments in the Production Test Region in the Shenhu sea, South China sea

Gas hydrate production testing was conducted in 2017 in the Shenhu Area in the northern part of the South China Sea, and unprecedented success was achieved. In order to obtain gas production and physical properties of gas hydrate reservoirs in the study area and determine the location of test production wells, the seismic and logging data and drilling cores were analyzed in detail, the physical characteristics of the sediments, faults, gas components, and reservoir were studied. The results show that 1) the gas hydrates are diffusion type, with reservoirs dominated by clayey silt sediments, and the gas hydrate-bearing layers are characterized by soup-like, porridge-like, cavity, and vein structures; 2) the resistivity and acoustic velocity of gas hydrate formation are significantly higher than those of the surrounding sediments, while the neutron porosity, density, and natural gamma are slightly lower; the Bottom Simulating Reflectors (BSRs) in seismic profiles exhibit the exist of gas hydrates; 3) gas chimneys and faults are well-developed beneath the BSRs, and hydrocarbon gases can easily migrate into the gas hydrate reservoirs in areas with stable temperature and pressure conditions; 4) the gas hydrate saturation is high, the highest saturation in site W17 was up to 76%, with an average of 33%; while the highest saturation in site W19 was up to 68%, with an average of 31%. The gas source is considered as mixed gas of thermogenic gas and microbial gas. By comparing the core samples and geophysical characteristics of sites W17 and W19 in the study area and calculating the thickness, distribution area, and saturation of the hydrate deposition layer, it was found that site W17 is characterized by a thick layer, large area, high saturation, and good sealing, and thus, site W17 was established as the test production site. The development of gas chimney and faults provides pathways for the upward migration of deep gas, and the gas migrates to gas hydrate stable zone in forms of diffusion, water soluble and free state, forming high saturation of diffusion gas hydrates.

Analysis of Gas Well Classifications and Productivity Influencing Factors in Kela-2 Gas Field

As one of the main gas fields in Chinese West-East Gas Transmission Project, cumulative gas production of Kela 2 Gas Field has been over 120 billion cubic meters since 2004. Each gas well has different gas production characteristics, water production, and pressure changes. Advanced water results in a serious non-homogeneous water invasion, and the actual gas production is much less than that in the development scheme. To further enhance the recovery of the Kela 2 Gas Field, productivity influencing factors of each gas pay interval are analyzed by using the production logging data based on geological characteristics. According to the change of the main gas pay and water production from production logging, gas wells are divided into four types, and three water invasion patterns in Kela 2 Gas Field. The causes of gas production changes and water production regime are analyzed for each type first, and then the main productivity influencing factors in gas wells are determined. The outcome shows that pressure drop, water breakthrough of gas pay layers, and different reservoir properties are the main productivity influencing factors. Through gas well classification of the Kela 2 Gas Field, the productivity influencing factors and productivity change regime are determined, which could provide grounds for further enhancing the recovery of the Kela 2 Gas Field.

Water saturation calculation using fractional flow and production logging data in a Caspian region sandstone petroleum reservoir

Water saturation in porous media plays a significant role in static, dynamic reservoir modeling and petroleum reserves calculations. In this paper, a Pliocene conglomerate type of sandstone Caspian reservoir was studied. The objective of this paper is to apply water saturation calculation method using fractional flow and PLT (Production Logging Tool) and to compare the results with other methods. In this study, 6 petrophysical rock typing were defined by two methods at log level using the porosity, water saturation logs and RQI(Reservoir Quality Index). Core porosity and permeability correlations were calculated for each rock type where k-phi transforms were generated, respectively. Relative Permeability data of the reservoir was classified based on the rock types. Oil-Water relative permeability ratios versus water saturation data were correlated exponentially. A solution for water saturation was derived at log level using fractional flow, relative permeability, core porosity, permeability and production data. Leverett-J function and Archie saturations were calculated to compare to the proposed fractional flow approach. The comparison of the saturation logs with J-function and Archie saturation showed good match. This approach can be considered as a tool to validate the saturation-J function. This methodology helps to estimate pre-production water cut performance of the wells in reservoir models considering that not all the wells will start flowing at 100% dry oil. The employed methods regarding the petrophysical rock typing and water saturation calculations will facilitate reservoir characterization for future field development activities.

Incorporating Dynamic Production-Logging Data to the Permeability-Estimation Workflow Using Machine Learning

Summary The objective of this work is to develop and train feedforward artificial neural networks (ANNs) on the forecasting of layer permeability in heterogeneous reservoirs. The results are validated by comparing the model outputs with permeability curves computed from production logging data. Production logs are used as targets to train the model. A flow-profile interpretation method is used to compute continuous permeability curves free of wellbore skin effects. In addition, segmentation techniques are applied to high-resolution ultrasonic image logs. These logs provide not only the image of the mega- and giga-pore system but can also identify the permeable facies along the reservoir. The image segmentation jointly with other borehole logs provides the necessary features for the network training process. The proposed neural network focuses on delivering reliable and validated permeability curves. Its development accounts for formation skin factor, as well as nongeological noise usually found in ultrasonic image logs. The procedure is tested on both synthetic and field data sets. The estimations presented herein demonstrate the model's ability to learn nonlinear relationships between geological input variables and reservoir dynamic data even if the actual physical relationship is complex and not known a priori. Although the preprocessing stages of the procedure involve some expertise in data interpretation, the neural-network structure can be easily coded in any programming language, requiring no assumptions on physics in advance. For the case studies presented in this work, the proposed procedure provides more accurate permeability curves than the ones obtained from conventional methods, which usually fail to predict the permeability measured on drill-stem tests conducted in dual-porosity reservoirs. The novelty of this work is to incorporate dynamic production-logging (PL) data into the permeability-estimation workflow. Correction Notice: The preprint paper was updated from its originally published version to correct Fig. 17 on page 11. An erratum detailing the change is included in the Supporting Information section below.

A Comprehensive Method for Diverter-Performance Evaluation during Stimulation of Long-Interval Heterogeneous Reservoirs: A Case Study

Summary The accomplishment of matrix stimulation in highly contrasted permeability reservoirs is critically dependent on diversion. Consequently, assessment of the diversion performance is a key to determine the success of stimulation. However, there are still doubts on the evaluation of diversion effectiveness, especially in long-interval heterogeneous reservoirs. When a diverter enters the formation, a hump in the surface pressure curve is usually expected. Then, it can be interpreted as supporting evidence for diversion. However, this is a simplification of the fluid-diversion process. It could be possible that a hump is not observed during a diversion stage, although it is effective. Therefore, what should be done? To overcome this challenge, we propose a more-accurate diversion-evaluation method and validate it with available matrix-stimulation data. Three methods were introduced in the literature to evaluate matrix-stimulation performance: Paccaloni, Prouvost, and Chan [inverse injectivity (i.e., Iinv)] methods (Prouvost and Economides 1987, 1989; Paccaloni and Tambini 1993; Chan et al. 2003). The latter is easy to use and accurate, which accounts for transient flow effects. In this paper, the inverse injectivity method is modified and validated with the real data of two matrix-acidizing operations in a gas/condensate field. The performed modifications in the evaluation process include a bottomhole-pressure-calculation procedure, which is validated with available drillstem-test (DST) matrix-stimulation data, and simultaneous utilization of Iinv and its derivative plot. Humps in the Iinv plot, which can be interpreted as diverter performance, are sometimes so small that it is difficult to distinguish the diverter effect from possible noises in the data. Here, the derivative plot of Iinv is used as a complementary tool to improve the interpretation process. Results indicate that for both wells in this study, the modified Iinv shows clear humps when diverters enter the reservoir. In addition, exactly when Iinv builds up, a sign change in the derivative plot is observed. This shows that these two parameters have a confirming behavior. Finally, pre/post-stimulation production data were used to practically prove the calculations behind the method. Here, the target of design was to divert stimulation fluids to the low-permeability bottom layer because it was both a high-pressure and high-hydrocarbon reserve. Per production-logging data, the majority of production before stimulation was originated from a sublayer. In the first operation, with the rare appearance of surface pressure humps, Iinv and its derivative showed satisfactory outputs of diversion occurrence. After stimulation, production logging confirmed the diversion of flow and nearly uniform production across the targeted interval. Hence, this indicates that the modified method accurately demonstrates the performance of the diversion system in acidizing operations with long perforated intervals, even if there is a rare distinct pressure hump in the surface. Therefore, this could be adapted either for cases where there is no access to the production logging or for the cases in which the hump in surface pressure is not observed.

Multiple-Source Data Provide Insight Into Hydraulic Fracture Geometries

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 195524, “Insight Into Hydraulic Fracture Geometries Using Fracture Modeling Honoring Field-Data Measurements and Post-Fracture Production,” by Neil Stegent, SPE, Ron Dusterhoft, SPE, and Hari Menon, SPE, Halliburton, prepared for the 2019 SPE Europec featured at the 81st EAGE Conference and Exhibition, London, 3-6 June. The paper has not been peer reviewed. Using planar fracture models to match treatment pressure and improve understanding of fracture-geometry generation is not a new concept. At some point during the progression from vertical to horizontal wellbores, however, many within the industry forgot about the learnings that still can be gained from current fracture models. The complete paper demonstrates the benefits of honoring data measurements from a multitude of potential sources to help engineers do a better job of including more diagnostics into routine operations to provide additional insight and result in improved models and completion designs. Introduction Many sources of reservoir, drilling, and completion data and measurements are available to well operators. However, in nearly all cases, a balance must be achieved of what can be captured at a reasonable cost and the associated value of the information gained. It is unrealistic to acquire high-density data sets for every well in an entire field because of the prohibitive costs, but understanding what is available and knowing what questions need to be answered can enable a more-cost-efficient means of acquiring the correct information to address specific challenges. Diagnostic Data and Measurements The complete paper contains a summary of some relevant tools and technologies and a short description of the information that can be captured, the importance of this information, and how it can be used; examples are discussed wherein some of these tools are combined to provide useful completion-design information. Gathering Production Information Production-logging tools are useful for characterizing and better understanding reservoir performance and optimizing production. The production logs are typically run after the completion of a well, either in a cased or open hole. The logs are used to determine the allocation of wellbore inflow to different zones and to identify areas of production problems such as crossflows and leaks. In low- and ultralow-permeability reservoirs with horizontal drilling and increases in the number of fracturing stages, understanding the production rate of each fracturing stage is important. Therefore, a post-fracturing evaluation might be necessary for optimization of multistage fracture design. Artificial-lift installations on post-fracture completions can be an obstacle to acquiring good production-log data in unconventional wellbores. Tracer Technologies Multiple tracer technologies are available for fracture diagnostics, and, while all such methods provide useful information, the results can be misleading when the methods are used independently. Conditions within the reservoir are changed significantly during the completion process and again during production. While wells can be in communication during hydraulic fracturing operations, that does not mean that the same wells will be in communication during production, because not all of the created fractures will remain open and conductive when production and reservoir depletion has begun.

Oil Phase Velocity Measurement of Oil-Water Two-Phase Flow with Low Velocity and High Water Cut Using the Improved ORB and RANSAC Algorithm

Abstract Velocity is an important parameter for fluid flow characteristics in profile logging. Particle tracking velocimetry (PTV) technology is often used to study the flow characteristics of oil wells with low flow velocity and high water cut, and the key to PTV technology is particle matching. The existing particle matching algorithms of PTV technology do not meet the matching demands of oil drops in the oil phase velocity measurement of oil-water two-phase flow with low velocity and high water cut. To raise the particle matching precision, we improved the particle matching algorithm from the oriented FAST and the rotated BRIEF (ORB) feature description and the random sample consensus (RANSAC) algorithm. The simulation and experiment were carried out. Simulation results show that the improved algorithm not only increases the number of matching points but also reduces the computation. The experiment shows that the improved algorithm in this paper not only reduces the computation of the feature description process, reaching half of the computation amount of the original algorithm, but also increases the number of matching results, thus improving the measurement accuracy of oil phase velocity. Compared with the SIFT algorithm and the ORB algorithm, the improved algorithm has the largest number of matching point pairs. And the variation coefficient of this algorithm is 0.039, which indicates that the algorithm is stable. The mean error of oil phase velocity measurement of the improved algorithm is 1.20 %, and the maximum error is 6.16 %, which is much lower than the maximum error of PTV, which is 25.89 %. The improved algorithm overcomes the high computation cost of the SIFT algorithm and achieves the precision of the SIFT algorithm. Therefore, this study contributes to the improvement of the measurement accuracy of oil phase velocity and provides reliable production logging data for oilfield.

ВТОРИЧНЫЕ КАРБОНАТЫ ЮРСКИХ ПЕСЧАНЫХ ОТЛОЖЕНИЙ КАК ПОКАЗАТЕЛИ ПРОДУКТИВНОСТИ ПАЛЕОЗОЯ

Работа основана на концепции глубинного генезиса флюидов с последующей субвертикальной миграцией (снизу вверх) по проницаемым зонам и разгрузкой в вышележащих коллекторах. Причем в зонах разгрузки и на путях миграции флюидов происходят геохимические преобразования аллотигенных минералов. Интенсивности процессов вторичного, наложено-эпигенетического образования минералов зависят от физико-химических свойств жидкости и скорости её течения. Целью статьи является иллюстрация связи интенсивности процессов вторичной карбонатизации юрских песчаных отложений с характером насыщения палеозойских пород. Методы. Интенсивность вторичной карбонатизации определялась по инновационной технологии статистической интерпретации материалов геофизических исследований скважин. Проводились сопоставления полученных статистических интенсивностей с результатами литолого-петрографических, минералогических исследований керна на предмет содержания карбонатов и с результатами испытаний палеозойских коллекторов. Актуальность работы обусловлена появившейся возможностью с высокой вероятностью выявлять зоны расположения нефтегазовых залежей в фундаменте по результатам статистической интерпретации данных геофизических исследований скважин юрских отложений, используя материалы старого фонда. Объектом исследования явились песчаные пласты юрского возраста Герасимовского месторождения (Томская область), а также карбонатные минералы, расположенные в песчаной породе. Выводы. Усреднённая интенсивность процесса вторичной карбонатизации в юрских песчаных отложениях является индикатором характера насыщения палеозойских пластов. С ростом интенсивности карбонатизации юрских пластов увеличивается вероятность обнаружения нефтенасыщенных залежей в фундаменте. С точки зрения выявления продуктивных залежей палеозоя наиболее информативным карбонатным минералом в юрских пластах является аутигенный сидерит.

Adjoint method acceleration protocols for model maturation to update static models with time-lapse reservoir surveillance data

We develop an ensemble model-maturation method that is based on the Randomized Maximum Likelihood (RML) technique and adjoint-based computation of objective function gradients. The new approach is especially relevant for rich data sets with time-lapse information content. The inversion method that solves the model-maturation problem takes advantage of the adjoint-based computation of objective function gradients for a very large number of model parameters at the cost of a forward and a backward (adjoint) simulation. The inversion algorithm calibrates model parameters to arbitrary types of production data including time-lapse reservoir-pressure traces by use of a weighted and regularized objective function. We have also developed a new and effective multigrid preconditioning protocol for accelerated iterative linear solutions of the adjoint-simulation step for models with multiple levels of local grid refinement. The protocol is based on a geometric multigrid (GMG) preconditioning technique. Within the model-maturation workflow, a deep-learning technique is applied to establish links between the mesh-based inversion results (e.g., permeability-multiplier fields) and geologic modeling parameters inside a static model (e.g., object dimensions, etc.). Our workflow integrates the learnings from inversion back into the static model, and thereby, ensures the geologic consistency of the static model while improving the quality of the ensuing dynamic model in terms of honoring production and time-lapse data, and reducing forecast uncertainty. This use of deep learning (DL) to post-process the model-maturation outcome effectively converts the conventional continuous-parameter history-matching result into a discrete tomographic inversion result constrained to geological rules encoded in training images.We demonstrate the practical utilization of the adjoint-based model-maturation method on a large time-lapse reservoir-pressure data set using an ensemble of full-field models from a reservoir case study. The model-maturation technique effectively identifies the permeability modification zones that are consistent with alternative geological interpretations and proposes updates to the static model. Upon these updates, the model not only agrees better with the time-lapse reservoir-pressure data but also better honors the tubing-head pressure as well as production logging data. We also provide computational performance indicators that demonstrate the accelerated convergence characteristics of the new iterative linear solver for adjoint equations.

Improved Understanding of Acid Wormholing in Carbonate Reservoirs through Laboratory Experiments and Field Measurements

SummaryA comprehensive study on wormholing has been conducted to improve the understanding of matrix acidizing in carbonate reservoirs. This work is a continuation of the previous work by Furui et al. (2012a, 2012b). An analysis of additional experimental results, as well as field measurements, is provided to reinforce and extend the wormhole-penetration model and productivity benefits provided by Furui et al. (2012b).A series of small-block tests and one large-block test under geomechanical stresses have been conducted to characterize wormholing in outcrop-chalk samples. In addition, field data including acid-pumping data and post-stimulation pressure-falloff data have been collected and analyzed to evaluate stimulation effectiveness. Pressure-buildup data from stimulated wells have also been analyzed to evaluate the sustainability of the acid-induced skin benefits. Production-logging data have been used to investigate whether created wormhole networks have remained stable or have collapsed under production stresses. To statistically analyze the data more comprehensively, the new data were also compared to field data available in the literature.The following conclusions are drawn from an analysis of the laboratory data and field data: (1) A skin value of −4 is achievable in carbonate reservoirs by matrix acidizing; (2) the negative acid skin is relatively stable under production stresses; (3) the wormhole-penetration model is proved to successfully simulate matrix-acidizing processes in both laboratory-scale and field-scale work; (4) the small- and large-block laboratory tests reconfirmed wormholing efficiency, which was discussed as a scale effect in the previous studies; and (5) an understanding of the possible range of wormhole penetration has allowed us to improve field acid treatments and reduce the risk of connecting to water.This comprehensive study includes acid-linear-coreflooding tests, small-block tests, large-block tests, and field measurements to thoroughly analyze acid wormholing in carbonate rock. The database can be very useful information for understanding, benchmarking, and optimizing future completion/stimulation design.

The Effect of Production-Logging-Tool Data on Scale-Squeeze Lifetime and Management of Scale Risk in Norwegian Subsea Production Wells: A Case Study

Summary Because of the higher cost of scale management for subsea (SS) operations compared with platform or onshore fields, and because of the more limited opportunities for interventions, it is becoming increasingly important to obtain and use real production data from wells rather than estimated zone flow contribution from simple permeability (k) and height (h) models for scale-squeeze-treatment design. In this paper I discuss how scale-squeeze treatments were designed (coreflood evaluation of inhibitor retention/release) and deployed for three SS heterogeneous production wells. A permeability model and a layer-height model were initially developed for each well using detailed geological log data, estimated water/oil-production rates, and the predicted water-ingress location within the wells. Two wells were each treated three times using bullhead scale-squeeze treatments, with effective scale control being reported over the designed lifetime. A production log was acquired before the fourth squeeze campaign of these two wells. This information was incorporated into the squeeze simulation to allow review of the ongoing third squeeze and enhance design accuracy for the upcoming fourth squeezes. A third well was treated twice before production-logging data became available, and the performance of treatments to this well is also assessed. The production-logging-tool (PLT) data proved very important in changing the understanding of fluid placement and the water-ingress location during production, resulting in changes to the isotherm values used to achieve effective history match to the inhibitor returns (with PLT data incorporated in all three wells), and most significantly affecting the squeeze lifetimes. It was possible to significantly extend the treatment lifetime of two of the wells (cumulative produced water to minimum inhibitor concentration), while the treatment life of one well was greatly reduced because of the PLT-data-modified model predictions. In this paper I outline the process of reservoir/near-wellbore modeling that is used for most initial squeeze-treatment service companies deployed in the North Sea. I will highlight in detail the value that PLT data can provide to improve the effectiveness of squeeze treatments in terms of understanding of fluid placement during squeeze deployment and water-ingress location within heterogenous production wells. The intention of this paper is to highlight the value that these types of data can provide to improve scale management (squeeze treatment and water shutoff) such that the value created more than offsets the cost of acquiring such information for SS production wells.