To obtain a reliable production forecast, one has to establish a geological model with well logs and seismic data. The geological model usually has to be upscaled using certain upscaling techniques. Then, a dynamic reservoir model is constructed with another dataset, including completion data, production data, fluid properties, and relative permeability curves. At last, the dynamic model needs to be validated by a history matching process. This approach is data-intensive, time-consuming, and often not rigorously accomplished due to the lack of skillset and time. In this study, 10,000 groups of reservoir/completion input data were generated by Latin hypercube sampling method, and then, 10,000 groups of output (oil rate and cumulative production data) were obtained by numerical simulation. Next, a machine learning technique was applied to establish a model between the input data and determining parameters of a decline curve analysis model by fitting the generated cumulative production rate. Overall coefficients of determination (
 
 
 
 R
 
 
 2
 
 
 
 ) of the three Arps decline curve factors were 0.966, 0.990, and 0.945. The validation result shows that the production rate and cumulative production predicted by the proposed machine learning–decline curve analysis (ML-DCA) model agreed well with those simulated by reservoir simulation. As a result of the ML-DCA regression model, a complete understanding can be established of the impact of reservoir properties on the DCA model. The proposed ML-DCA model not only provides a quick and robust method for petroleum engineers to estimate production performance for unconventional reservoirs from reservoir and completion properties without full-field geocellular modeling but also can be used to optimize the completion and operation parameters for wells of interest.

/pdf/development-of-decline-curve-analysis-parameters-for-tight-2mh8ad59.pdf

Development of Decline Curve Analysis Parameters for Tight Oil Wells Using a Machine Learning Algorithm

Bridging the integration gap—simultaneous optimization of well placement, well trajectory, and facility layout

Integration of Reservoir Performance and Geoscience Studies in the History Match of a Complex Carbonate Reservoir – a Case Study from the Magwa Marrat Reservoir - Kuwait


 This paper highlights an unconventional approach of using DPDP (Dual Porosity Dual permeability) simulation technique for modelling hydraulic fractures in a full field simulation model during the forecast analysis performed on a tight carbonate reservoir in Kuwait. This was a part of integrated study in which ‘multi-stage hydraulic fracturing’ was recommended as the most optimum stimulation technique in order to enhance the productivity of all the proposed horizontal producers. Importance of DPDP model increases multi-fold when contrast between fracture and matrix permeability is in the order of 10 times or more. In the studied case, as average matrix permeability of the reservoir is in the range of 2-3 mD, this contrast is magnified to the order of 1000-10000 times (considering fracture permeability is in Darcies) which further complements the use of DPDP model.
 Three different approaches were tried to model the impact of multi-stage hydraulic fracturing in the full field simulation model; 1) ‘Enhance Well PI’ for all the stimulated wells, 2) ‘Enhance Matrix Permeability’ in the vicinity of all the stimulated wells, hereby referred as SPSP (Single Porosity Single Permeability) approach, and 3) build ‘DPDP Model’ by using upscaled fracture porosity and fracture permeability without changing the matrix properties. First two approaches are very common in the industry but most of the times are not able to capture the real impact of hydraulic fracturing on flow behaviour (bi-linear flow), whereas DPDP model is designed to capture the flow through dual medium. In both SPSP and DPDP approaches permeability anisotropy (increased permeability in the direction perpendicular to horizontal section of the well) in the fractured zone was very well captured and was needed to honour the hydraulic fractures direction. Fracture permeability was calculated using the Poiseuille's law; few sensitivity cases were run to address the associated uncertainty.
 Field cumulative oil production and recovery factor were analysed for ‘Enhanced Well PI’ case, SPSP cases and DPDP cases. Field oil cumulative production in DPDP cases is 6% more than SPSP cases and around 10% more than ‘Enhanced Well PI’ case. The hypothesis for the higher recovery in DPDP case with respect to other two cases is that bi-linear flow (fractures are getting filled with the matrix fluid and then feeding to well) is better represented in the DPDP model. Impact in this case is more significant due to the big contrast between matrix and fracture permeability.
 Low capacity with high conductivity signature of hydraulic fracture is difficult to model in the SPSP or just by enhancing the well PI. Study clearly demonstrated the benefits of DPDP model for modelling hydraulic fractures over the conventional methods.

Modelling Hydraulic Fractures in a Full-Field Dynamic Model Using DPDP Simulation Techniques - An Unconventional Approach Applied in a Tight Carbonate Oil Reservoir, Kuwait, Middle-East

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Frederic Lislaud

Author Tools

Chat about Author

Papers

Integration of Reservoir Performance and Geoscience Studies in the History Match of a Complex Carbonate Reservoir – a Case Study from the Magwa Marrat Reservoir - Kuwait

Modelling Hydraulic Fractures in a Full-Field Dynamic Model Using DPDP Simulation Techniques - An Unconventional Approach Applied in a Tight Carbonate Oil Reservoir, Kuwait, Middle-East