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Taha, Taha (Emerson Automation Solutions) | Ward, Paul (Emerson Automation Solutions) | Peacock, Gavin (Emerson Automation Solutions) | Heritage, John (Emerson Automation Solutions) | Bordas, Rafel (Emerson Automation Solutions) | Aslam, Usman (Emerson Automation Solutions) | Walsh, Steve (Emerson Automation Solutions) | Hammersley, Richard (Emerson Automation Solutions) | Gringarten, Emmanuel (Emerson Automation Solutions)
This paper presents a case study in 4D seismic history matching using an automated, ensemble-based workflow that tightly integrates the static and dynamic domains. Subsurface uncertainties, captured at every stage of the interpretative and modelling process, are used as inputs within a repeatable workflow. By adjusting these inputs, an ensemble of models is created, and their likelihoods constrained by observations within an iterative loop. The result is multiple realizations of calibrated models that are consistent with the underlying geology, the observed production data, the seismic signature of the reservoir and its fluids. It is effectively a digital twin of the reservoir with an improved predictive ability that provides a realistic assessment of uncertainty associated with production forecasts.
The example used in this study is a synthetic 3D model mimicking a real North Sea field. Data assimilation is conducted using an Ensemble Smoother with multiple data assimilations (ES-MDA). This paper has a significant focus on seismic data, with the corresponding result vector generated via a petro-elastic model. 4D seismic data proves to be a key additional source of measurement data with a unique volumetric distribution creating a coherent predictive model. This allows recovery of the underlying geological features and more accurately models the uncertainty in predicted production than was possible by matching production data alone.
A significant advantage of this approach is the ability to utilize simultaneously multiple types of measurement data including production, RFT, PLT and 4D seismic. Newly acquired observations can be rapidly accommodated which is often critical as the value of most interventions is reduced by delay.
The aim of this study is to determine to what extent the quality of a history matched model is a good predictor of future production. The background is the common assumption that the better a model matches the production data is the better it is for forecasting, or, at the very least, it leads to an improved estimate of the uncertainty in future production. We demonstrate that the validity of this assumption depends on the length of the history match period and that of the forecasting period. It also depends on how heterogeneous the reservoir is.
The correlation between the quality of history match and quality of forecast depends on various factors. For the same level of heterogeneity one of the strongest factors is the water breakthrough time for the base and compared cases.
Broadly if both the base and compared case have water breakthrough before the end of the history match period then the forecasts are reasonable. However, there appears to be a very rapid transition from a reasonably good history match leading to a good forecast to a moderately good history match leading to a very poor forecast. If water breakthrough has not occurred there is a very poor correlation between the quality of the history match and the quality of the forecast. So, the traditional belief that a good history matched model will also produce a good forecast is not always true.
The Bahrain Oil Field was the first oil discovery in the Gulf Region in 1932 and is now in a mature stage of development. Crestal gas injection in the oil bearing, under saturated, layered and heavily faulted carbonate Mauddud reservoir has continued to be the dominant drive mechanism since 1938. Thirty eight 40 acre 5-spot waterflood patterns were implemented from 2011 to 2012. These patterns were located in both the South East and North West part of the Mauddud reservoir with a maximum injection rate of 80,000 bbl/day. With less than 10% PV water injected as of December 2012, premature water breakthrough was observed in most of the producers. Rapid water breakthrough in Mauddud A (Ba) is attributed to presence of high permeability vugs and layers resulting in water cycling and poor sweep in the matrix leaving bypassed oil. Following recommendations from the 2013 partner Peer Assist, the South East and North West waterfloods have been converted from pattern to peripheral with downdip wells providing water injection. Peripheral re-alignment has arrested the production decline, reduced water cut and stabilized production.
Surveillance data such as bottomhole pressure data, production logs, reservoir saturation logs, temperature logs and tracer data form the basis of understanding waterflood performance. Additionally, an array of analytical tools were used for diagnosis and analysis. Amongst the diagnostic tools, the Y- function helped to understand water cycling and sweep; the modified-Hall plot assisted in understanding the high-permeability channel or lack thereof and the water-oil-ratio (WOR) provided the clue on fluid displacement. Additional plots such as the "X" plot, decline curve, Cobb plot, pore volume injected vs. recovery, Jordan plot, and Stagg's plot were generated to gain insight on the waterflood.
Based on the waterflood analysis, a field study was initiated in December 2016 by shutting more than 80% of water injection followed by complete shut-in in September 2017. The purpose was to reduce the water cut, improve production taking advantage of gravity drainage effect of gas injectors located up dip of waterflood areas. The implementation of water injection shut-in is still ongoing in the Bahrain Field and pressure/production performance is being closely monitored. Improved production performance is observed following water injection shut-in.
This study underscores the importance of modern analytical tools to diagnose and analyze waterflood performance. This understanding also paves the way for much improved learning to take appropriate actions and help devise long-term reservoir management strategy.
As the world's population grows, its thirst for water continues to increase. Yet, the total amount of freshwater that is available naturally does not replenish quickly enough to match this growth. Consequently, increase in water consumption and continuous drainage has resulted in a growing shortage for industrial use.
In the oil and gas industry, significant volumes of water are used to carry out various treatments such as water injection, matrix acidizing, and multistage hydraulic fracturing. Fresh water is clean and contains low salt content, making it the ideal water source to mix these treatments with ease. However, as unconventional and tighter formations are developed, the use of water in such treatments exponentially increases. Trucking, piping and shipping freshwater for oil and gas purposes becomes both uneconomical and unethical, especially for offshore operations.
Seawater has been gaining attention as a viable alternative to freshwater in the oil and gas industry. Ideally, the most cost-effective way is to use raw seawater. However, raw seawater contains ions and microorganisms that can introduce or exacerbate scaling, corrosion, bacterial problems, and most importantly hinder desired fluid performance. Therefore, seawater is typically treated to remove adequate quantities of these components. Since treatment of seawater is increasing in popularity, it is important to realize which components are necessary to remove, to achieve a balance between treatment cost and treatment benefit.
In this review, a complete picture of seawater as an alternative to fresh water will be presented. This includes examining the reason for using seawater, the challenges faced, the technologies developed, and many applications of seawater based treatment fluids. Through this, readers should be able to gain a complete picture of the problem at hand and the solutions available to tackle it.
Water plays an extremely important role in many industries ranging from textiles to the oil and gas operations. Particularly in the oil and gas industry, fresh water is ideal to synthesize treatment fluids and is frequently used. However, to meet the needs of a growing population and to compensate for the declining reserves of fresh water, the cost of using fresh water has increased significantly. Furthermore, in areas without readily available sources of freshwater, the cost incurred transporting fresh water to the field can be significant. Therefore, the industry is increasingly turning to alternative sources of water to meet their requirements. One such alternative is seawater, which is a popular choice due to its natural abundance and low transportation cost, especially in coastal areas.
Olatunde, Folarin (Chevron Nigeria) | Adeyinka, Adeboye (Chevron Nigeria) | Lawal, Olumide (Chevron Nigeria) | Iyiola, Sunkanmi (Chevron Nigeria) | Faparusi, Dan (Chevron Nigeria) | Bodunrin, Abiodun (Chevron Nigeria) | Sustakoski, Richard (Chevron Nigeria) | Ebo, Henrietta (Chevron Nigeria)
Time-lapse seismic survey also known as 4D seismic has established itself as a useful tool for reservoir monitoring and has gained wide acceptance within the industry. Technological advancements in the area of acquisition and processing have further strengthened the case for its application. The recent 4D seismic acquisition and interpretation in Agbami has proven to be an economically viable means of adding tremendous value to an oil field irrespective of the development stage it is in, and has been an excellent enabler for reservoir surveillance and resolution of subsurface uncertainties. Effective management of a field such as Agbami requires a surveillance method which can provide insight into spatial fluid movements with time, which the traditional surveillance methods are unable to provide. This type of insight is required to support sound reservoir management and field development decisions which Agbami 4D seismic provides.
Construction of predictive reservoir models involves subjective interpretation and interpolation of spatially limited data, often using imperfect modeling assumptions. Hence, the process can introduce significant uncertainty and bias into production predictions. In particular, the uncertainty associated with the facies distribution in complex geologic environments, such as fluvial channels, can be consequential for production forecasting and reservoir development planning. Conventional history matching techniques are mainly designed to update continuous reservoir properties, such as permeability and porosity distributions. As such, calibrating discrete facies against production history may require a parametrization approach to convert discrete facies to continuous parameters. In this paper, we present a distance transformation method for calibrating the distribution of discrete facies againts production data. Distance-based transforms are widely used in discrete image processing, where the discrete values in each image pixel are replaced with their distance (i.e., a continuous variable) to the nearest cell with a different value (i.e., facies boundary). The history matching is then performed by updating the continuous distance maps based on observed production data. Once the distances are updated, an inverse distance transform is applied to convert them back to discrete facies models. The distance transformation enables discrete facies updating with history matching techniques that are designed for continuous variables, while ensuring that the final solution remains discrete. A low-rank representation of the continuous distance maps with the truncated singular value decomposition (SVD) is also presented for problems in which large-scale facies connectivity is expected. In that case, the production data is used to update the low-rank SVD projection coefficients of the distance maps. The developed discrete transformation ensures that the solution of the facies model calibration problem remains discrete. Furthermore, the use of prior models and SVD parameterization help to preserve the connectivity in the geologic facies after dynamic data integration. Examples with two and three facies models are used to illustrate the application of the method and to evaluate it performance.
Kumar, Sarwesh (Chevron Corporation) | Wen, Xian-Huan (Chevron Corporation) | He, Jincong (Chevron Corporation) | Lin, Wenjuan (Chevron Corporation) | Yardumian, Hrant (Chevron Corporation) | Fahruri, Irvan (Chevron Corporation) | Zhang, Yanfen (Chevron Corporation) | Orribo, Jose M. (Chevron Corporation) | Ghomian, Yousef (Chevron Corporation) | Marchiano, Iryna Petrovska (Chevron Corporation) | Babafemi, Ayanbule (Chevron Corporation)
Reservoir simulation is a widely accepted tool for assessing the impact of uncertainties on upstream investment decisions. Currently, the most widely used workflow addressing these uncertainties is a traditional two-step approach: 1) geoscientists performing static uncertainty analysis with earth modeling parameters and selecting a few representative geological models (for example, low-mid-high); 2) reservoir simulation engineers conducting dynamic uncertainty analysis with dynamic parameters combined with the pre-selected geological models and performing history matching, forecasting, or optimization. In this workflow, all the geological uncertainties are lumped into one parameter (the grid) for use in the second step. This severely reduces the flexibility for considering a wider range of alternative static realizations, and thus may bias the history match and forecasts. We implemented an integrated workflow, called "big-loop" that unifies the two-step approach into a single step. This allows for simultaneous and explicit analysis of both types of uncertainties and improvement in reservoir management decision quality. It also allows for direct modification of earth model parameters to achieve a history match with geological consistency. Although the concept is not new to the industry, it is rare to find references of field applications of the "big-loop" workflow. We present the applications of this workflow to both green and brown reservoirs to demonstrate its value in improving accuracy and efficiency. In a Gulf of Mexico green field, the workflow is applied for uncertainty analysis of static parameters (for example sand channel width and salt body extension) and dynamic parameters (for example rock-fluid properties) for probabilistic Original Oil in Place (OOIP) assessment and production forecast. The workflow facilitates the design of uncertainty resolution, upside capture and downside mitigation plans. In an onshore fractured reservoir, the workflow is applied for simultaneous history matching using static fracture parameters (fracture length and aperture) and dynamic parameters. The workflow improves the model accuracy and decision quality for the upcoming IOR/EOR development project. In an offshore gas field, the workflow is used to perform experimental design (ED) studies with static and dynamic uncertainties. This systematic & automatic workflow eliminates manual inputs and reduces the need for recycles. Finally, in another field, the workflow is used to perform probabilistic history matching using static and dynamic parameters. This workflow is capable of delivering a full-cycle solution for uncertainty assessment and probabilistic history matching with high efficiency and high quality results.
Asawapayukkul, S. (Chulalongkorn University) | Laochamroonvorapongse, R. (PTT Exploration and Production PLC) | Pancharoen, M. (PTT Exploration and Production PLC) | Rattanarujikorn, Y. (PTT Exploration and Production PLC) | Tivayanonda, V. (PTT Exploration and Production PLC) | Palviriyachote, S. (PTT Exploration and Production PLC) | Sopitkamol, W. (PTT Exploration and Production PLC) | Akrapipatkul, P. (PTT Exploration and Production PLC)
Waterflood has been long considered as a cost-effective way to significantly add worldwide reserves. Oil displacement by injected water is a main mechanism of waterflood; however, common factors including vertical heterogeneity, interwell connectivities, and lack of injection and production control lead to less-than-expected flood performance. The intensive data monitoring and interpretation are a key to understand ongoing flood performance. The case study of Sirikit oil field is a good example of waterflood in complex multilayer thin-bedded reservoirs.
The use of diagnostic plots and analytical tools were applied to investigate ongoing waterflood in different levels (area, reservoir, and well). The sweep efficiency plots were used to compare the waterflood efficiency among 13 different areas in Sirikit field. The impacts of key parameters including injection duration, pore volume injection, reservoir depositional environment, level of reservoir depletion when waterflood started, drive mechanism, and flood pattern on incremental recovery factor were scrutinized. In a reservoir level, the estimated recovery factor from production data, reservoir and fluid properties, and Modified-Y plot were compared to give more prudent numbers. The capacitance-resistance model (CRM) was selected to understand the travelling directions of injected water from production and injection data. In well level analysis, this study expands the vertical heterogeneity measurement from the use of core data to injection logging data via the Lorenz plots and the Dykstra-Parson coefficients to capture conformance effects. The vertical heterogeneity maps were constructed from calculated coefficients and well locations to shred some lights on the distribution of reservoir complexity. In addition, Hall's plot was selected to diagnose injection problems.
From analysis, the depositional environment plays an important role in sweep efficiency as areas with clastic braided reservoirs yield higher recovery gain from waterflood than areas with fluvio-deltaic reservoirs. The plots of recovery gain versus pore volume injection highlighted the areas with relatively poorer flood performance, which require further mitigation plans. The results suggest the sooner waterflood started, the higher recovery gain is anticipated. Waterflood in solution gas drive reservoir tends to yield higher incremental recovery gain from waterflood than gas cap drive reservoir. The recovery factor calculated from analytical methods confirms the numbers estimated from a decline curve analysis method. CRM indicates that most injected water (70% to 100%) flows toward producers, which shows good interwell connectivities. The well analysis suggests the spatial distribution of moderate to high vertical heterogeneity. Hall's plots indicate that the 70% of total injectors have experienced injection problems mainly from plugging.
These insights are critically beneficial for future waterflood screening and evaluation, reservoir management, well conformance control, and injector treatment. This quick analysis will assist not only the waterflood performance improvement but also the future EOR decisions.
Analyzing well performance is a complex process that increase in difficulty when multiple reservoir-drive mechanisms are in play in the same reservoir. This paper explores an overpressured, compacting chalk reservoir with high porosity and high oil saturation at initial conditions. The diverse drive mechanisms, experienced through the long production history of Valhall Field in Norway, are caused by different degrees of reservoir compaction across the field and the recent waterflood at the crest and northern areas of the field. The purpose of this study is to illuminate the various drive mechanisms experienced in this field. The underlying objective is to understand widely varying Arps b-factors in decline-curve analysis (DCA) that support production forecasting and project evaluation. The performances of inactive wells with long production histories were used as analogs to analyze active wells. Other analytical tools also were used to augment overall understanding of a type well’s performance, including rate-transient analysis (RTA) and capacitance/resistance modeling (CRM). This study demonstrates that the proposed work flow for reservoir-performance forecasting can be adopted in highly complex reservoirs with different rock-mechanical properties, drive mechanisms, production scheduling, and field-development strategies. Specifically, the work flow entails establishing energy support for individual wells by use of Arps b-factor with DCA; collapsing shut-in periods, if any, and using the cumulative production curve for DCA to retain solution objectivity; performing RTA to gauge pressure/rate coherence and system’s linearity; and using CRM to establish injector/producer connectivity.
Reservoir E is an offshore saturated black-oil reservoir with three lobes, initially thought of as independent reservoirs, but later seen as lobes of same reservoir with acquisition of more pressure data. It started production in 1968, but was shut-in in 1985 due to declining pressure. After the onset of waterflood in 1992, pressure data showed the reservoir behaving like two pressure tanks aerially (Main and Horn Area). The main area showed good pressure response from water-flooding but limited response was observed in the horn area. The challenge has been how to improve the current reservoir performance given the availability of reserves especially in the horn area which is pressure challenged.