Integrating Rock Typing Methods Including Empirical, Deterministic, Statistical, Probabilistic, Predictive Techniques and New Applications for Practical Reservoir Characterization

This paper discusses integrating common methods and applications for "Rock Typing" (also known as Petrophysical Rock Typing-PRT) including empirical, deterministic, statistical, probalistic and automatic/predictive approaches. Many industry asset teams apply one or more of these methods when creating static reservoir models, using dynamic reservoir simulations, completing petrophysical studies for saturation height models and determining reservoir volumetrics as part of reservoir characterization studies. Our intention is to provide guidance and important information on how and when to use the various methods, so people can make an informed selection. This discussion is important as many disciplines apply these PRT techniques without understanding the pros, cons and limitations of the different methods. An important tool is comparing PRT results from multiple methods. The topics and workflows that are covered focus on various PRT techniques and workflows. We will use case-studies to illustrate the key features and make important comparisons. Key results include comparing pros and cons, how to use and combine multiple PRT techniques and verify results. This paper includes these techniques and workflows;MICP, core analysis and pore throat calibration.Core-Log Integration focused on PRT analysis.Winland, Pittman, Aguilera and Hartmann et.al Gameboard methods.K-Phi ratio, Flow Zone Indicators and Rock Quality Index methods.Classic, Modified and Stratigraphic Lorenz methods.IPSOM and HRA Probabilistic methods.Case Study – Super Plot and Advanced Automatic PRT Method.Special Topics – Carbonate Methods, NMR and Single Well Vertical Line. Practical approaches based on case studies show how PRT analysis can be applied in mature fields to identify by-passed hydrocarbon zones and zones that have a high probability of producing water using open hole, cased hole and production logs. Traditional Rock Typing (PRT) analysis can be applied as a single well technique or as a multi-well method so operations teams can identify additional business opportunities (remedial workovers, infill drilling locations or exploitation targets) and compare reservoir performance with intrinsic rock properties. New applications and additional topics cover single, multiple well approaches and new emerging PRT techniques (including NMR well logs and machine learning). We recommend how to merge classic facies with PRT analysis for 3-D applications including populating a 3D volume.

Recovery Factor Improvement; A Success Story of Improving 10% of RF in Greater Natih Reservoirs, North of Oman

Recovery Factor Improvement (RFI) is a process to check the hydrocarbon production efficiency by incorporating the actual static and dynamic field data, as well as the way how the field being operated. This has been a common process within Shell's portfolio since 2018 (Ref; Muggeridge et al., 2013 & Smalley et al., 2009). The approach has been developed to stimulate the identification of new opportunities to increase the recovery from the existing fields and to aid the maturation of these opportunities into the Opportunity Realization Process. There are four (4) factors that affected overall reservoir recovery factor, they are: Pressure efficiency; related to which pressure can be reduced in the reservoir as dictated by the relevant facilities and wells.Drainage Efficiency; the proportion of the in-place hydrocarbon that is pressure-connected directly to at least one producing well on a production timescale.The "secondary pay" efficiency; takes into account the volumes of poorer quality rock in which the gas remains at pressure above the lowest pressure just outside the wellbore (Pf) when the reservoir is abandoned.Cut-off Efficiency; the proportion of hydrocarbon that is lost due to non-production of the tail.This approach was applied in the dry gas Natih Reservoir fields in the PDO concession area. Before the implementation of RFI, the average recovery factor for Natih was around 70%. This was considered low for a homogenous-dry gas reservoir. The targeted Natih fields were benchmarked against each other with a total of 11 fields with similar reservoir properties. Post the benchmarking exercise, the expected field recovery factor is approximately ~90-93%. The team managed to map out the opportunities to achieve the targeted RF and identified the road map activities. The activities are mainly related to: production optimization: retubing, re-stimulation reduce drainage: infill drilling, horizontal well reduce the field intake through compression The outcome of the mapping was then further analyzed through integrated framework to be matured as a firm-project. The new proposed activities are expected to add around 9% additional recovery to the existing fields. There will be a remaining activities which will be studied in the future, example infill wells and intelligent completions. These will close the gap to TQ and add other addition RF of 11-13%. As conclusion, the RFI was seen as a structured approach to better understanding the field recovery factor based on the integrated surface and subsurface data with a robust analysis to trigger opportunity identification linked to RFI elements. It is similar concept as sweating the asset by generating limit diagram for each recovery mechanism & the road map to achieve the maximum limit. This paper will highlight the Natih Fields RFI analysis, highlighting the key learning and challenges.

Intelligent Well Drainage and Interference Density Analysis Tool for Optimizing Infill Drilling

During the progressive development of mature fields, it is imperative to drill many infill wells to accelerate production and access bypassed oil. Optimizing the infill well spacing is always the concern to reduce interference with existing wells and improve recovery. In the present study, using intelligent data mining techniques, a new analysis and visualization tool has been developed and implemented to estimate and map drainage radius by well to assess the efficiency of the current development pattern and properly plan future wells. The tool deployed several performance-based techniques to estimate the contacted stock-tank oil initially in place (STOIIP) by each existing well, and outcomes can be compared between techniques for validation. The contacted STOIIP is then converted into an effective drainage radius by well using reservoir properties from the geo-cellular model. The evaluated reservoir is subdivided vertically into pay zones drained by the wells based on geological barriers/baffles to flow and connectivity across the zones. The tool estimates drainage radii of the wells produced from the reservoir using five different methods. The resultant Proved Developed Producing (PDP) reserves polygon maps are generated for the connected zones. The drainage radii of wells with behind-casing opportunities are estimated based on correlation and adjacent wells methods, and Proved Developed Non-Producing (PDNP) reserves polygon maps were generated. Well interference density is estimated based on overlapping drainage radii polygons with adjacent well locations, which has then been validated with production and pressure data from the wells. This paper describes the methodology by which the well drainage radii and well interference density can be estimated and implemented on a selected reservoir. This workflow can be successfully used to identify drained and undrained areas around the wellbore and opportunities for additional infill wells in the various pay zones of the reservoir. This exercise observed consistency in the drainage radii cumulative distribution from decline curve analysis methods and the No-Further-Activities (NFA) simulation case.

An integrated approach to evaluating the effectiveness of infill drilling of the Ust-Tegusskoye field

The article considers the experience of using infill well patten in the territory of Western Siberia. The justification of geological and geotechnical factors affecting the efficiency of infill drilling with the subsequent use of a sector-crushed hydrodynamic model of the field site is given. With the help of the identified criteria, promising areas of infill drilling of wells are mapped, and it is established that increasing the detail of the grid of the hydrodynamic model makes it possible to clarify the localization of remaining oil in place. Based on the obtained results from the hydrodynamic model, variants of adjusted planned well count are compared according to accumulated and annual indicators. Thus, the infill well drilling program is optimized. The implementation of an integrated approach to the selection of sites for compaction drilling and the use of a detailed hydrodynamic model of this site allows to increase the production efficiency of recoverable remaining oil in place, as well as to level the risks of oil production.

Production Performance Analysis of Volga-Ural Mature Waterflood with PRIME Diagnostic Metrics

The paper presents a practical case of production performance analysis at one of the mature waterflood oil fields located at the Volga-Ural oil basin with a large number of wells. It is a big challenge to analyse such a large production history and requires a systematic approach. The main production complication is quite common for mature waterflood projects and includes non-uniform sweep, complicated by thief injection and thief water production. The main challenge is to locate the misperforming wells and address their complications. With the particular asset, the conventional single production analysis techniques (oil production trend, watercut trend, reservoir and bottom-hole pressure trend, productivity trend, conventional pressure build-up surveys and production logging) in the vast majority of cases were not capable of qualifying the well performance and assessing of remaining reserves status. The performance analysis of such an asset should be enhanced with new diagnostic tools and modern methods of data integration. The current study has made a choice in favor of using a PRIME analysis which is multi-parametric analytical workflow based on a set of conventional and non-conventional diagnostic metrics. The most effective diagnostics in this study have happened to be those are based on 3D dynamic micro-models, which are auto-generated from the reservoir data logs. PRIME also provided useful insights on well performance, formation properties and the current conditions of drained reserves which helped to select the candidates for infill drilling, pressure maintenance, workovers, production target adjustments and additional surveillance. The paper illustrates the entire PRIME workflow, starting from the top-level field data analysis, all the way to generating a summary table containing well diagnostics, justifications and recommendations.

Streamlining the Well Location Optimization Process - An Automated Approach Applied to a Large Onshore Carbonate Field

Infill drilling is a proved strategy to improve hydrocarbon recovery from reservoirs to increase production and maximize field value. Infill drilling projects address the following questions: 1) Where should the wells be drilled? 2) What should be their optimum trajectories? 3) What are the realistic ranges of incremental production of the infill wells? Answering these questions is important yet challenging as it requires the evaluation of multiple scenarios which is laborious and time intensive. This study presents an integrated workflow that allows the optimization of drilling locations using an automated approach that comprises cutting-edge optimization algorithms coupled to reservoir simulation. This workflow concurrently evaluates multiple scenarios until they are narrowed down to an optimum range according to pre-set objectives and honoring pre-established well design constraints. The simultaneous nature of the workflow makes it possible to differentiate between acceleration and real incremental recovery linked to proposed locations. In addition, the technology enables the optimization of all the elements that are relevant to the selection of drilling candidates, such as location, trajectory, inclination, and perforation interval. The well location optimization workflow was applied to a real carbonate large field; heavily faulted; with a well count of +400 active wells and subject to waterflooding. Hence the need for an automated way of finding new optimal drilling locations enabling testing of many locations. Also due to the significant full field model size; sector modelling capability was used such that the optimization, i.e. running many scenarios; could be carried out across smaller scale models within a reasonable time frame. Using powerful hardware and a fully parallelized simulation engine were also important elements in allowing the efficient evaluation of ranges of possible solutions while getting deeper insights into the field and wells responses. As a result of the study, 8 out of the original 9 well locations were moved to more optimal locations. The proposed optimized locations generate an incremental oil recovery increase of more than 70% compared to the original location (pre-optimization). In addition, the project was completed within 2 weeks of equivalent computational time which is a significant acceleration compared to a manual approach of running optimization on a full field model and it is significantly more straight forward than the conventional location selection process. The novelty of the project is introduced by customized python scripts. These scripts allow to achieve practical ways for placing the well locations to explore the solution space and at the same time, honor well design constraints, such as maximum well length, maximum step-out from the surface well-pad, and well perforation interval. Such in-built flexibility combined with automation and highly advanced optimization algorithms helped to achieve the project goals much easier and faster.

Reservoir Management Paradigm Shift Upon Successful Recent Drilling Well of Sungai Gelam Field, Jambi

Sungai Gelam structure is one of the backbone brownfield structures supporting Jambi field oil productions. Geologically, Sungai Gelam is highly related to structural-trap type which commonly occur in Air Benakat Formation, as main hydrocarbon producer. There are total 29 wells in Sungai Gelam penetrate the Air Benakat Formation, some extend through the Talang Akar Formation. Re-evaluation of the last two years (S-25 and S-26) of infill drilling program indicate unsatisfactory production results. The latest two wells which have been drilled in 2018 have been used to update velocity model, facies model and the reservoir simulation. Considering tremendous depth uncertainty on the western part of the field, several new infill well locations have now been planned to recover bypassed oil within the existing wells, to acquire new velocity data and to be water injection conversion-ready location for the productive reservoirs. The overall reservoir management approach has been thought to be the most benign option for the field. Well S-27 has been approved in 2019 as one of the best infill locations. The well location bears the lowest risks and produces a naturally flowing 286 BOPD far beyond the predicted oil target. It also yields a 2040 psia virgin formation pressure oil column from new N1 sand productive target which have not fully developed by the existing wells. The discovery leads to a speedy work over program at the existing nearby well, S-23, and produces 212 BOPD with 0% water cut. Two infill wells acceleration have been proposed for year 2020. The field’s reservoir characterization study has been yet again recycled by the new target oil. The field has now been under drastic redevelopment plan with more detailed reservoir flow unit modeling, new data acquisition, PSDM seismic reprocessing, new infill wells and step-out wells targeting deeper reservoirs. Sungai Gelam field development shows strong fundamental yet versatile field reservoir management rendering to real-time drilling data. New findings have been seamlessly adjusted in the framework and acted upon accordingly. Production of S-27 and S-23 well then accelerate additional two drilling wells which drilled in 2020.

Success story of integrated subsurface study to deliver a successful infill drilling: Buntal-5 case study

Buntal is a mature gas field located in South Natuna Sea Block B PSC. The field was discovered by well Buntal-1 and delineated by appraisal well Buntal-2. The field consists of multi-stacked sandstone reservoirs, which were deposited under fluvial deltaic environment. The major Buntal reservoirs have been produced since 2004 from two subsea wells. Buntal-3 was producing from zones Beta-1 and Beta-2, while Buntal-4 was a horizontal well producing from Zone-1C. Both of those wells had loaded up prior to Buntal-5 drilling. This paper describes the details of a multidisciplinary approach taken for the proposal of Buntal-5 infill drilling. An integrated geological and geophysical study were carried out to quantify resources and uncertainties of the remaining thin unproduced zones. In total, there are 8 virgin zones as Buntal-5 initial target namely Beta-0, Zone-1A, Zone-1B, Zone-1D, Zone-1E, Zone-2B, Zone-3 and Zone-3A. Max-trough seismic amplitude was utilized to identify geological features across for each Buntal reservoir. The result was then combined with geological concept based on its depositional environment to justify a reasonably higher hydrocarbon volume which can not be estimated only by wells’ data. A reservoir simulation study was also carried out to not only to evaluate production potential from the virgin zones but also to capture upside potential from the produced zones. Simulation history matching result on Zone-1C revealed early water breakthrough experienced by Buntal-4 well due to water cresting phenomena which left significant gas reserves. This result added upside potential to Buntal-5 which initially only targeted marginal remaining unproduced zones. The well was drilled at the end of 2019 and proven to be a major success. Buntal-5 open hole logs data indicate thicker and better virgin zones reservoir quality as expected by integrated geological and geophysical study. Furthermore, significant remaining gas was encountered in Zone-1C with actual gas water contact was within the simulation result proving the water cresting theory, the zone itself add well’s gas-in-place by 30% on top of the unproduced zones’ gas-in-place.

Numerical Investigation of Major Impact Factors Influencing Fracture-Driven Interactions in Tight Oil Reservoirs: A Case Study of Mahu Sug, Xinjiang, China

Fracture-driven interactions (FDIs) in unconventional reservoirs significantly affect well production and have thus garnered extensive attention from the scientific community. Furthermore, since the industry transitioned to using large completion designs with closer well spacing and infill drilling, FDIs have occurred more frequently and featured more prominently, which has primarily led to destructive interference. When infill wells (i.e., “child” wells) are fractured, older, adjacent producing wells (i.e., “parent” wells) are put directly at risk of premature changes in production behavior. Some wells may never fully recover following exposure to severe FDIs and, in the worst case scenario, will permanently stop producing. To date, previous investigations into FDIs have focused mainly on diagnosis and detection. As such, their formation mechanism is not well understood. To address this deficiency, a three-dimensional, multi-fracture propagation simulator was constructed based on the unconventional fracture model (UFM) and applied to a system that included both an older, adjacent passive well (“parent” well) and an active well (“child” well). Herein, the theoretical framework for overall complex fracture modeling is described. Furthermore, numerical simulation results are presented, demonstrating the critical roles of in-situ stress distribution and pre-existing natural fractures and aiding in the development of appropriate strategies for managing FDIs.