Dynamic Reservoir Models Research Articles

Generative Well-Pattern Design Presents Opportunities To Reduce Drilling Expenditures

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 203152, “Generative Well-Pattern Design Applied to a Giant Mature Field Leads to the Identification of Major Drilling Expenditure Reduction Opportunities,” by Maddalen Lepphaille, Total; Arthur Thenon, Modis; and Pierre Bergey, Total, et al., prepared for the 2020 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, held virtually 9–12 November. The paper has not been peer reviewed. A generative well-pattern-design (GWPD) work flow was benchmarked against traditional manual designs to leverage three reservoir-development planning opportunities applicable to a giant mature Middle Eastern carbonate field. People remained central in ensuring efficient problem setup and exploration guidance, but the work flow proved able to identify substantially better patterns than the traditional approach for each of the opportunities at the cost of only a few hundred simulations. GWPD Overview The work flow used to tackle the different problems of this study, which the authors call the GWPD “well-improvement scheme” (WISH), consists of the following steps. Each step is detailed in the complete paper. 1. Definition of design space 2. Constraint of design space 3. Qualification of design space 4. Nondominated sorting (a specific ranking of all of the cells of the con-strained design space according to the value of their quality indicators) 5. Candidate design investigation 6. Investigation of preferred designs 7. Optimization of preferred design GWPD Application Context. The application study was con-ducted at the beginning of the industrialization of WISH, a proprietary software tool dedicated to GWPD. The authors call the work flow GWPD-WISH. In the studied oil field, more than 400 oil producers and water- or gas-injector strings have been drilled from approximately 100 platforms in a series of reservoirs. The study focused upon two specific reservoirs holding most of the field reserves. These reservoirs are developed with peripheral water injection and gas injection into the gas cap. According to the latest development plan, hundreds of wells will be drilled during the next 40 years in order to maintain a production plateau. The context was deemed favorable because a 3D gridded dynamic reservoir model was available, the geology and development history defined a large and complex design problem, and large liquid hydrocarbon reserves were thought to remain. While the software and method used enables considering an ensemble of realizations capturing reservoir uncertainties, only a single history-matched realization of the model was available. Consequently, the study did not deal with reservoir uncertainties.

Dynamic reservoir sand characterization of an oil field in the Niger Delta from seismic and well log data

Many oil fields within the Niger Delta have been abandoned after serving their estimated life time largely due to large uncertainties and risks associated with many subsurface complexities and often limited and/or inconsistent surveillance data coupled with the convectional static characterization of the reservoir bodies as is the case of the X- oil Field, south eastern onshore, Nigeria. This research presents a pragmatic approach of integrated dynamic reservoir modelling with uncertainty management techniques, which resulted in robust productivity of the abandoned oil field. The objective is to run and improve comprehensive sensitivity analysis of petrophysical properties in the reservoir, develop PVT analysis, and predict acceptable original oil in place (OOIP), comprehensive production history and expected cumulative oil recovery factor (RF). Integrated dynamic reservoir modelling of static interpreted reservoirs of two vertically stacked reservoirs (A and B) was done. The analysis identified reservoir A with an OWC at 5520 ft with thickness of about 300 ft and STOIIP of 59 MMSTB has an underlying reservoir B with OWC at 6100 ft with thickness of about 300 ft and STOIIP of 21 MMSTB. Average permeabilities and porosities of both reservoirs range from 2.54 to 1115.2 md and 0.2 to 0.33 respectively, with estimated cumulative oil production of 46.62 MMSTB at 55% RF in reservoir A and 17.21 MMSTB at 48% RF in reservoir B. This was estimated to correspond to maximum cumulative oil production of 40 MMSTB by August 2020 indicating that the overall recovery from the field could potentially be increase by 6% from drilling infill wells with drainage targets. Production data analysis indicates that the recovery factors and well recoverable volumes are highly correlated to average net oil pay. The correlations may be used for reserve quality control. It is recommended that improved quality of data couple with integration of statics with dynamic modelling should be a priority to reduce uncertainties and solve problems in situ in field operations leading to increase in oil recovery during activation of abandoned oil field.

Evaluation of enhanced coalbed methane recovery and carbon dioxide sequestration potential in high volatile bituminous coal

Geologic sequestration of carbon dioxide (CO2) captured from flue gas into deep unmineable coal seams is a promising option to mitigate greenhouse gas emissions. Also, the injection costs can be partially or entirely offset by the revenue generated through the additional methane recovered. Complex phenomena occur during primary coalbed methane (CBM) recovery and CO2 injection for enhanced methane production and CO2 sequestration (CO2-ECBM). Numerical modeling and simulations can be used to represent these complex phenomena.This research investigates the CO2 storage capacity and CO2-ECBM recovery potential of a high volatile bituminous coal (Ferron coal) in Buzzard Bench, Uinta Basin. It is one of the most recognized coalbed methane producing areas in the U.S. and high-graded for evaluation due to its proximity to coal-fired power plants and CBM activity history. A three-dimensional geological model was first constructed with information available from the public domain. Available gamma-ray and density logs were utilized to develop the three-dimensional model of the coal seam and the overlying rocks using geostatistical techniques. This allows heterogeneity to be introduced in both vertical and horizontal directions, thereby creating a representational geologic model. After that, a dynamic reservoir model was constructed and calibrated against actual methane production using a history match. Simulations of CO2 injection and collateral methane production were carried out after the history match using two injectors and two production wells.The fluid flow simulation results demonstrated that it may be feasible to inject 1.16 million tons and sequester 1.15 million tons of CO2 over 20 years. Additionally, 13.95 billion cubic feet of methane were recovered within the same period. Due to the permeability reduction by coal matrix swelling, the methane recovered decreased by 19%, while the volume of CO2 injected decreased by 29%. This demonstrated injection feasibility, even in the presence of potential permeability reduction by coal matrix swelling during CO2 injection. Injection and production strategies are suggested to maximize injectivity and sequestration to overcome problems resulting from permeability changes and override.

AI, Data Analytics Enable Approach to History Matching in Geologically Complex Field

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 202460, “History Matching of Production Performance for Highly Faulted, Multilayered, Clastic Oil Reservoirs Using Artificial Intelligence and Data Analytics: A Novel Approach,” by Nis Ilyani Mohmad and Dipak Mandal, SPE, Petronas, and Hadi Amat, Petroliam Nasional, et al., prepared for the 2020 SPE Asia Pacific Oil and Gas Conference and Exhibition, originally scheduled to be held in Perth, Australia, 20-22 October. The paper has not been peer reviewed. History matching is a critical step for dynamic reservoir modeling to establish a reliable, predictive model. Numerous approaches have emerged over decades to accomplish a robust history-matched reservoir model. As geological and completion complexity of oil and gas fields increase, building a fully representative predictive reservoir model can be arduous to almost impossible. The complete paper outlines an approach to history matching that uses artificial intelligence (AI) with an artificial neural network (ANN) and data-driven analytics. The new approach has been used to mitigate history-matching challenges in a mature, highly geologically complex field offshore Malaysia. Overview The complete paper describes a step-by-step methodology for building a reservoir model and a history-matching process using ANN. The paper discusses data preparation and quality assurance and control (QA/QC), spatiotemporal database formulation, reservoir model design, ANN architecture design, model training, and history-matching strategy. A case study of implementation in an offshore Malaysian field is presented, wherein good-to-fair history-matching quality was obtained for all production parameters. Field A is a highly geologically complex, 25×75-km oil sandstone reservoir with more than 200 major and minor faults and more than 30 reservoir layers. It has been producing for more than 25 years. The challenges of history matching this field lie not only in its geologically complex structure and corresponding subsurface uncertainties, but also in a production strategy that has involved commingled dual-string production with several integrity issues that exacerbate the field’s complexities. To date, Field A has no fieldwide history-matched reservoir model because the complexity of history matching has precluded using conventional numerical simulation methods. This challenge has been mitigated by implementing an AI-based reservoir model and data analytics. The new approach is estimated to be more time- and cost-efficient than the conventional method. The complete paper compares the AI-based and conventional numerical reservoir modeling approaches and highlights the advantages, limitations, and areas of improvement of the new methodology. The authors present their case for AI-based reservoir modeling as a complement or alternative to conventional numerical modeling to create time-efficient reservoir models while reducing risk in field-development plans.

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.

New Generation of Pulsed-Neutron Multidetector Comparison in a Challenging Multistack Clastic Reservoir: A Case Study in Brown Field, Malaysia

Running pulsed-neutron logs in Malaysia has previously been plagued by results with high uncertainties, especially in brown fields with complex multistacked clastic reservoirs. Together with a wide range of porosities and permeabilities, the acquired logs quite often tended to yield inconclusive results. In addition, the relatively fresh aquifer water (where salinity varies from 5,000 to 40,000 ppm) makes reservoir fluid typing and distinguishing between oil and water even more challenging. As a result, the inconsistencies and uncertainties of the results tend to leave more questions than answers. Confidence in using pulsed-neutron logging, especially to validate fluid contacts for updating static and dynamic reservoir models, deteriorated within the various study teams. Due to this fact, the petrophysics team took the initiative to conduct a three-tool log off in one of their wells with the objective of making a detailed comparison of three pulsed-neutron tools in Malaysia’s market today. The main criteria selected for comparisons were the consistency of the data, repeatability, and statistical variations. With recent advancements in pulsed-neutron (multidetector) tool technology, newer tools are being equipped with more efficient scintillation crystals, improving the repeatability of the measurements as well as the number of gamma ray (GR) count rates associated with the neutron interactions. In addition, the newer tools now have up to five detectors per tool, with the farthest detector supposedly being able to “see” deeper into the formation, albeit at a lower resolution. With these new features in mind, the log off was conducted in a single well with a relatively simple completion string (single tubing, single casing), logged during shut-in conditions only, and the logs were acquired directly one after the other (back to back) to avoid bias to any particular tool. Both sigma and spectroscopy measurements were acquired to compare the capabilities of each tool. Due to the relatively freshwater salinity, the carbon-oxygen (C/O) ratio from the spectroscopy measurements is used to identify the remaining oil located in the reservoirs, while the sigma measurements determine the gas-oil or gas-water contact, if present. This paper will illustrate the steps taken by Petronas Carigali Sdn Bhd (PCSB) to compare the raw data and interpreted results from the three pulsed-neutron tools. Consequently, a comparison from all the tools was made to the current understanding of the reservoir assessed. The points from these comparisons will then show which tools are favored over the rest.

First time-lapse OBN in Bonga deepwater offshore field

Time-lapse seismic data from Bonga Field, located in deepwater Nigeria, have delivered excellent results previously from dedicated streamer seismic surveys (2000, 2008, and 2012) that image changes in amplitude due to water replacing oil. One challenge with the streamer data is the presence of a floating production storage and offloading (FPSO) unit. It is difficult to accurately repeat streamer acquisition geometry in economically important updip regions of reservoirs that lie beneath the FPSO. To ensure an accurate repeat of this area, we acquired an ocean-bottom node (OBN) survey in 2010 and carried out the first time-lapse OBN repeat of the survey in 2018. Time-lapse processing of the OBN data produced excellent results. We obtained an OBN-on-OBN normalized root mean square (NRMS) difference repeatability of 6%. This was an improvement over the 12% NRMS for streamer-on-streamer repeats. The OBN data quality was unaffected by the FPSO, enabling us to properly image the changes occurring in this area. The 4D OBN interpretation was used to identify bypassed oil opportunities. The data also were used to derisk well placement, optimize water injection, and update the dynamic reservoir models. The new models are essential to enhance predictability and field performance. We also analyzed an area northwest of the field where we extended the 2018 OBN acquisition and compared it to streamer data to optimize a new injection well location.

A time-lapse case study in West Africa: Integrating disciplines for a complete reservoir study and field management

Development and management of deepwater oil fields requires material investment and must be supported by application of quality technological solutions and tools. To maximize the advantages that can derive from these, it is essential to integrate technologies and disciplines throughout the field cycle. This integration makes it possible to identify and fix issues early and seize business opportunities quickly. We describe the activity carried out on a field characterized by a high-quality oil-bearing clastic reservoir located in a deepwater belt along a West African continental passive margin. We highlight how geoscience specialists from both geologic and geophysical branches have contributed to construction of the static and dynamic reservoir model, field monitoring, model updating, and operations optimization. The wide geologic and geophysical experience in the area made it possible to build a petroelastic model and a sedimentological model that were integrated in the population of a geocellular model. Seismic data also were used to support reservoir monitoring of the producing field. A dedicated 4D seismic project helped our understanding of fluid movement and fault behaviors in the field. The 4D information was interpreted and shared within the integrated team with the goal of identifying further business opportunities and driving decision making over our area of interest.

Development of Hybridized Completions for Extended Reach Horizontal Wells

Non-uniform production and injection profiles in extended reach horizontal wells invite several production and recovery issues. Downhole flow control devices, along with dynamic reservoir modeling, have been beneficial in regulating flow, improving productivity from the toe section, delaying water breakthrough, reducing water coning, and improving overall reservoir sweep. However, such measures add to substantial completion costs and may not be economical for marginal reservoirs. Using simple slotted liners is a cheaper option but may not be effective in regulating injection/production profiles in the longer term. This research focused on applying “coupled static and dynamic modeling” to examine and compare five different types of completion designs, using data from a heterogeneous carbonate reservoir. Results show that inflow control device (ICD) integrated completions can achieve better recovery than the slotted, pre-perforated, or engineered liners. Engineered-slotted liners perform better than the pre-perforated-slotted liners. The pre-perforated-slotted liners do not show much improvement over open-hole completions. Finally, a hybrid completion design is optimized by combining ICD with engineered-slotted liners, which showed higher well productivity, lower water cut production, and reduced completion cost.

Deep net simulator (DNS): a new insight into reservoir simulation

Reservoir simulation plays a vital role as a diagnostics tool to better understand and predict a reservoir’s behaviour. The primary purpose of running a reservoir simulation is to replicate reservoir performance under different production conditions; therefore, the development of a reliable and fast dynamic reservoir model is a priority for the industry. In each simulation, the reservoir is divided into millions of cells, with fluid and rock attributes assigned to each cell. Based on these attributes, flow equations are solved through numerical methods, resulting in an excessively long processing time. Given the recent progress in machine learning methods, this study aimed to further investigate the possibility of using deep learning in reservoir simulations. Throughout this paper, we used deep learning to build a data-driven simulator for both 1D oil and 2D gas reservoirs. In this approach, instead of solving fluid flow equations directly, a data-driven model instantly predicts the reservoir pressure using the same input data of a numerical simulator. Datasets were generated using a physics-based simulator. It was found that for the training and validation sets, the mean absolute percentage error (MAPE) was less than 15.1% and the correlation coefficient, R2, was more than 0.84 for the 1D oil reservoirs, while for the 2D gas reservoir MAPE < 0.84% and R2 ≈1. Furthermore, the sensitivity analysis results confirmed that the proposed approach has promising potential (MAPE < 5%, R2 > 0.9). The results agreed that the deep learning based, data-driven model is reasonably accurate and trustworthy when compared with physics-derived models.

Quantifying the impact of the structural uncertainty on the gross rock volume in the Lubina and Montanazo oil fields (Western Mediterranean)

Abstract. Structural uncertainty is a key parameter affecting the accuracy of the information contained in static and dynamic reservoir models. However, quantifying and assessing its real impact on reservoir property distribution, in-place volume estimates and dynamic simulation has always been a challenge. Due to the limitation of the existing workflows and time constraints, the exploration of all potential geological configurations matching the interpreted data has been limited to a small number of scenarios, making the future field development decisions uncertain. We present a case study in the Lubina and Montanazo mature oil fields (Western Mediterranean) in which the structural uncertainty in the seismic interpretation of faults and horizons has been captured using modern reservoir modeling workflows. We model the fault and horizon uncertainty by means of two workflows: the manually interpreted and the constant uncertainty cases. In the manually interpreted case, the zones of ambiguity in the position of horizons and faults are defined as locally varying envelopes around the best interpretation, whose dimensions mainly vary according to the frequency content of the seismic data, lateral variations of amplitudes along reflectors, and how the reflectors terminate around faults when fault reflections are not present in the seismic image. In the constant case, the envelope dimensions are kept constant for each horizon and each fault. Both faults and horizons are simulated within their respective uncertainty envelopes as provided to the user. In all simulations, conditioning to available well data is ensured. Stochastic simulation was used to obtain 200 realizations for each uncertainty modeling workflow. The realizations were compared in terms of gross rock volumes above the oil–water contact considering three scenarios at the depths of the contact. The results show that capturing the structural uncertainty in the picking of horizons and faults in seismic data has a relevant impact on the volume estimation. The models predict percentage differences in the mean gross rock volume with respect to best-estimate interpretation up to 7 % higher and 12 % lower (P10 and P90). The manually interpreted uncertainty workflow reports narrower gross rock volume predictions and more consistent results from the simulated structural models than the constant case. This work has also revealed that, for the Lubina and Montanazo fields, the fault uncertainty associated with the major faults that bound the reservoir laterally strongly affects the gross rock volume predicted. The multiple realizations obtained are geologically consistent with the available data, and their differences in geometry and dimensions of the reservoir allow us to improve the understanding of the reservoir structure. The uncertainty modeling workflows applied are easy to design and allow us to update the models when required. This work demonstrates that knowledge of the data and the sources of uncertainty is important to set up the workflows correctly. Further studies can combine other sources of uncertainty in the modeling process to improve the risk assessment.

Machine learning to reduce cycle time for time-lapse seismic data assimilation into reservoir management

Time-lapse (4D) seismic is widely deployed in offshore operations to monitor improved oil recovery methods including water flooding, yet its value for enhanced well and reservoir management is not fully realized due to the long cycle times required for quantitative 4D seismic data assimilation into dynamic reservoir models. To shorten the cycle, we have designed a simple inversion workflow to estimate reservoir property changes directly from 4D attribute maps using machine-learning (ML) methods. We generated tens of thousands of training samples by Monte Carlo sampling from the rock-physics model within reasonable ranges of the relevant parameters. Then, we applied ML methods to build the relationship between the reservoir property changes and the 4D attributes, and we used the learnings to estimate the reservoir property changes given the 4D attribute maps. The estimated reservoir property changes (e.g., water saturation changes) can be used to analyze injection efficiency, update dynamic reservoir models, and support reservoir management decisions. We can reduce the turnaround time from months to days, allowing early engagements with reservoir engineers to enhance integration. This accelerated data assimilation removes a deterrent for the acquisition of frequent 4D surveys.

Integrated Asset Model Enables Simulation of Complex, Multifield Asset

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 192603, “New-Generation Integrated Asset Modeling: High-Resolution Reservoir Multimodels Coupled With an External Steady-State Network Solver,” by Pietro Selvaggio, Fabrizio Freni, Roberto Rossi, Danielle Christian Di Giorgio, and Ivan Colombo, Eni, prepared for the 2018 Abu Dhabi International Petroleum Exhibition & Conference, Abu Dhabi, 12–15 November. The paper has not been peer reviewed. This paper presents an innovative application of the integrated-asset-model (IAM) approach to simulate a surface network collecting many complex fields and multiple constraints. The use of a last-generation high-resolution reservoir simulator (HRRS) made it possible to perform the reservoirs-network coupling directly by a field-manager (FM) process, which acts as an orchestrator for a variety of reservoirs and network-simulation instances. No third-party controller application was required because the FM of the HRRS directly managed the communication between the different reservoir models and the surface-network model. Introduction The ability to predict reservoir behavior has always played a fundamental role in the definition of a development project, and numerical simulation can be considered the first tool for a quantitative evaluation of reservoir performance. In the past decades, improvements in information technology have increased calculation capabilities, enabling development of dynamic simulators able to solve for more-complex reservoirs and dynamic processes. During the production-forecast phase, for example, technical constraints must be considered, primarily related to hydrocarbon rate (i.e., gas sale limitation, gas/water injection limits, flaring policies, limitations in export pipelines, and treatment capacity) and pressures (i.e., minimum required pressure to reach a treatment, separator working conditions, and export-pipeline design). The increasing development of fields located in remote or offshore areas and the growing interest in near-field exploration make it necessary to exploit common surface networks. In these situations, the previously mentioned technical constraints and limitations must be defined for the entire asset, thus accounting for the intrinsic connection between fields that become interdependent. With standard standalone reservoir simulation no longer able to identify and address these kinds of problems and constraints, integrating the reservoir and network is key to an optimized development strategy. The IAM brings together reservoir, well, and surface-facility models in a unique system for reservoir and well optimization. This methodology ensures that the interactions between all the components are accounted for correctly and, by coupling dynamic reservoir and facilities models into a single IAM, addresses backpressure, the mixing of different fluids, and flow assurance. Currently, two approaches are avail-able to perform the integration. The first is based on combining the reservoir models with a dedicated network simulator with optimization capabilities in a fully integrated model. The second approach is focused on using the capabilities offered by some commercial simulators in which the network solver, on the basis of precomputed hydraulic tables, is included in the simulator together with some features of reservoir coupling.

Warping least-squares inversion for 4D velocity change: Gulf of Mexico case study

We have developed a new trace-based, warping least-squares inversion method to quantify 4D velocity changes. There are two steps to solve for these velocity changes: (1) dynamic warping with phase constraints to align the baseline and monitor traces and (2) least-squares inversion for 4D velocity changes incorporating the time shifts and 4D amplitude differences (computed after trace alignment by warping). We have demonstrated this new inversion workflow using simple synthetic layered models. For the noise-free case, phase-constrained warping is superior to standard, amplitude-based warping by improving trace alignment, resulting in more accurate inverted velocity changes (less than 1% error). For synthetic data with 6% rms noise, inverted velocity changes are reasonably accurate (less than 10% error). Additional inversion tests with migrated finite-difference data shot over a realistic anticline model result in less than 10% error. The inverted velocity changes on a 4D field data set from the Gulf of Mexico are more interpretable and consistent with the dynamic reservoir model than those estimated from the conventional time-strain method.

Combined Study of Static and Dynamic Reservoir Modelling for the CO2 Storage Project in Deep Saline Aquifer in Zhundong, Xinjiang, China

Geological storage of CO2 has been identified as an effective technology to reduce greenhouse gas emissions and mitigate global climate change. Deep saline aquifers are recognised as having the highest CO2 storage potential. The Junggar Basin is located in the northern Xinjiang and has extensive distributed deep saline aquifers, which could be the effective sites for CO2 storage. CO2 injectivity and storage capacity were investigated through both static and dynamic modelling on the Cretaceous Donggou Formation aquifer in Zhundong area, Junggar Basin. A static reservoir model was constructed by integrating well data and seismic attributes, and the best estimate of storage capacity (P50) was estimated to be approximately 72 million tonnes using a storage coefficient of 2.4% (P50). Dynamic simulation provided a comprehensive understanding of injectivity, storage capacity and explanation of the different storage mechanisms after CO2 injection. The total injection of CO2 was 31.4 million tonnes with five injection wells. Simulations suggest that at year 300 after injection, 28% of the injected CO2 was stored by residual trapping and 26% of the injected CO2 was dissolved into formation water. The modelling results suggest that there is good potential for large scale CO2 aquifer storage in the Junggar Basin.

Intelligent Completion in Water-Injector Well Improves Field Development

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 192850, “Improving Field Development Through Successful Installation of Intelligent Completion in a Water-Injection Well,” by Eglier Yanez, Mattheus Uijttenhout, Maher Zidan, Rail Salimov, Salem Al-Jaberi, Al Anoud Al-Shamsi, Amnah Al-Sereidi, Mohamed Mostafa Amer, Yousef Al-Hammadi, Abdullah Abdul-Halim, Giovani Caletti, Mustapha Adli, Yousif Hasan Al-Hammadi, and Fahad Mustafa Al-Hosani, ADNOC, prepared for the 2018 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 12–15 November. The paper has not been peer reviewed. The operator piloted a new well-completion design combining inflow-control valves (ICVs) in the shallow reservoir and inflow-control devices (ICDs) in the deeper reservoir, both deployed in a water-injector well for the first time in the company’s experience. The objectives were to improve reservoir management, reduce well-construction complexity, and achieve cost optimization. This paper covers the overall aspects of well performance in commingled injection mode. Introduction The offshore field was discovered in 1971 in the Arabian Gulf. On the basis of results from exploratory wells, three main reservoirs were proposed for further development (A, B, and C). The commercial phase of the field started successfully, with first oil in 2015 provided by the two first wellhead towers as part of the Phase I development plan. The completion design described in this paper is aligned with an optimized field-development plan (FDP) resulting from a study initiated in 2017. The main objectives of the study were integrating new acquired data to upgrade static and dynamic reservoir models to identify any opportunity for extra production and cost optimization. Early water breakthrough caused by the initial elected well pattern and the associated oil-producer-to-water-injector spacing is a significant risk. Therefore, accessibility and selectivity to control inflow are strongly recommended for the upper reservoir (A) to mitigate the breakthrough risk and secure long well life and maximize reserves. The initial dual completion, with two horizontal laterals targeting Reservoirs A and B, and a nonaccessible upper drain was removed, and a single controlled completion was placed. Field-Development Plan Pattern. The FDP consisted of two phases. Phase 1 consisted of an early production scheme from two wellhead towers and export of untreated crude to nearby facilities. This phase contained an intensive data-gathering program to reduce reservoir uncertainties and identify future risks and opportunities clearly. Phase 2 is the full field development, containing the bulk of the oil producers and water-injector (WI) wells drilled from new wellhead towers and newly installed offshore treatment facilities. The overall reservoir performance of Reservoir A was significantly better compared with the original basis of assumption. Consequently, the original line-drive development scheme between oil producers and WIs had to be revised in order to avoid early water breakthrough. As a response, the injector wells were placed outside the oil pool. The behavior of Reservoir B met expectations; for that reason, the line-drive well pattern was maintained in this reservoir.

A novel flow-based geometrical upscaling method to represent three-dimensional complex sub-seismic fault zone structures into a dynamic reservoir model

Most hydrocarbon reservoirs contain faults, which are highly complex heterogeneous and anisotropic three-dimensional (3D) volumes of deformed rock. A major technical challenge in full-field flow simulation is to represent the effects of 3D fault zone structure within the two-dimensional (2D) fault planar surfaces using the industry standard commercial reservoir flow simulator due to its limited functionality. Therefore, a new flow-based geometrical upscaling (FBGU) method has been developed for capturing the effects of 3D fault zone structures in conventional low-resolution upscaled flow simulation models. Geometrical upscaling (GU) is the process of calculating across-fault and up-fault transmissibility arising from 3D flow paths through fault zones, and expressing these transmissibilities as implicit connections in a low-resolution upscaled flow simulation model. The high accuracy of the method is demonstrated by comparing the flow responses of high-resolution (referred as truth model in this paper) simulation models in which the 3D fault zone structure is represented explicitly in the grid geometry, with that of conventional resolution models in which it is upscaled using FBGU method. The flow results show that the newly developed FBGU method is extremely accurate and geometrically flexible.

Integrating multi-disciplinary data for building fit-for-purpose 3D mechanical earth model

Understanding the influence of geomechanics early in the field development phase facilitates reservoir management planning. To capture complex geology and associated field development, a 3D mechanical earth model (3D MEM) with finite element method (FEM) approach was selected to analyse the geomechanical-related risks associated with two fields in the North West Shelf, Australia. The 3D MEMs were constructed using geological static models, and seismic-derived horizons and faults. The 3D properties were propagated based on core-calibrated 1D properties and controlled by stratigraphy, 3D facies and seismic inversion volumes. The FEM was used to calculate the equilibrium of stresses and strains within the 3D MEMs. The 3D properties and pre-production stresses were validated in blind test wells before forward modelling. The 3D MEMs were linked to the dynamic reservoir models to capture the pressure evolution throughout the field lifecycle. The results were used to analyse the risks associated with compaction, subsidence, fault instability, completion integrity and drilling stability of infill wells through depleted reservoirs. The results provided insight in managing the risk early in field development stage. The study’s largest challenge was integrating a large volume of data to ensure that the structural complexity and rock heterogeneity were captured and consistent with the geological understanding of the field. A multi-disciplinary team of earth scientists and reservoir and geomechanics engineers worked together, and the value of data integration, good communication and teamwork were key success factors. Lessons learned and best practices were captured throughout the study and provided valuable feedback for future works.

Impact of sequence stratigraphy on static and dynamic reservoir models: examples from the Precipice-Evergreen succession, Surat Basin, Queensland

CO2 storage in the subsurface is a key aspect of climate mitigation. The UQ is investigating whether the Precipice Sandstone and Evergreen Formation in the Surat Basin, Queensland, are an appropriate reservoir-seal pair for the long-term storage of greenhouse gases. However, the Precipice-Evergreen succession remains poorly constrained from a paleo-depositional and stratigraphic standpoint. Studies have mostly applied lithostratigraphy for local correlation, and the understanding of time-stratigraphic relationships across the basin needs development. This has greatly hindered the capacity to construct robust reservoir models and is an active area of research.We utilized core, wireline logs, seismic reflection surveys, and pressure data to compare the dynamic response to various CO2-injection scenarios with contrasting stratigraphic architectures. A lithostratigraphic prediction of reservoir and seal intervals consisted of a layer-cake model of fluvio-deltaic deposits. The models suggest that reservoir layers are laterally well-connected with the gas plume primarily migrating parallel to bedding. In contrast, a sequence stratigraphic arrangement of facies resulted in a more complex architecture, where reservoir and non-reservoir strata cross-cut and intersect one another. The resulting models showed greater reservoir heterogeneity and potentially more complex fluid transmission pathways in both the lateral and vertical directions that could result in slower plume migration and more residual trapping. This is due to the fact that discontinuous mudstone intervals potentially baffle the CO2 plume and may allow for more CO2 trapping within the lower parts of Evergreen succession. The contrasting models show different geological realizations arising from the same dataset, interpreted in different ways than identify where there is uncertainty. They may highlight certain areas of the basin are more conducive to carbon storage than others.Fluid flow is highly sensitive to the stratigraphic arrangement of reservoir and non-reservoir intervals. Refining static and dynamic models using sequence stratigraphy may result in a significant improvement in history matching. Modellers should carefully consider the implications of stratigraphic correlations during static model construction.

Analysis of Time-Lapse Seismic and Production Data for Systematic Reservoir Model Classification and Assessment

The heterogeneous distribution of reservoir properties is one of the most important uncertainties in static and dynamic reservoir modelling. Petrophysical properties are usually interpolated within reservoir models from sparse well-log data, which can lead to highly uncertain estimates at inter-well locations that directly affect the reliability of fluid-flow model predictions of reservoir behavior. To address this issue, we build an ensemble of equiprobable models that combine different geostatistical realizations of reservoir properties to span the range of potential outcomes. While this process captures the impact of reservoir property distributions on the model response, a major challenge is classifying the subset of models in the ensemble best representing reservoir fluid-flow behavior. Thus, we introduce a methodology combining 4D seismic amplitude attributes and reservoir production data to classify fluid-flow models. This classification is based on applying thresholds for independent seismic and production objective functions. We test our methodology on the benchmark case UNISIM-I developed from observations from the Namorado Field, Campos Basin, Brazil. By comparing injection and production rates in relation to 4D seismic amplitude trends, we identify nine models out of an ensemble of 100 that judged optimal via the required seismic and production objective function thresholds and obtain an improved quantitative evaluation of the impact of reservoir production on the 4D seismic signal. Ultimately, combining seismic and production data offers interpretation scenarios that automatically identify realistic fluid-flow models that can assist the update of permeability and porosity distributions within the reservoir.